Active Learning for Neural PDE Solvers
Authors: Daniel Musekamp
Marimuthu Kalimuthu
David Holzmüller
Makoto Takamoto
Mathias Niepert
Venue: ICLR 2025 Poster (2025)
Keywords: Active Learning
Neural PDE Solvers
Scientific Machine Learning
Benchmark
Framework
Neural Operators
Primary area: datasets and benchmarks
TL;DR: A extensible benchmark to evaluate pool-based active learning for neural PDE solvers.

Abstract: Solving partial differential equations (PDEs) is a fundamental problem in engineering and science.  While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain. Active learning (AL) could help surrogate models reach the same accuracy with smaller training sets by querying classical solvers with more informative initial conditions and PDE parameters. While AL is more common in other domains, it has yet to be studied extensively for neural PDE solvers. To bridge this gap, we introduce AL4PDE, a modular and extensible active learning benchmark. It provides multiple parametric PDEs and state-of-the-art surrogate models for the solver-in-the-loop setting, enabling the evaluation of existing and the development of new AL methods for PDE solving. We use the benchmark to evaluate batch active learning algorithms such as uncertainty- and feature-based methods. We show that AL reduces the average error by up to 71\% compared to random sampling and significantly reduces worst-case errors. Moreover, AL generates similar datasets across repeated runs, with consistent distributions over the PDE parameters and initial conditions. The acquired datasets are reusable, providing benefits for surrogate models not involved in the data generation.

Published as a conference paper at ICLR 2025
ACTIVE LEARNING FOR NEURAL PDE SOLVERS
Daniel Musekamp" 1:3
Marimuthu Kalimuthu 12,33 David Holzmüller*
Makoto Takamoto
Mathias Niepert!.2.33
Universitt Stuttgart TemT IMPRS-IS
"INRIA Paris, Bcole Normale Supérieure, PSL University daniel. musekampkk kimp.pkii...e uni wtsstsuutttrr.....
'NEC Labs Europe
ABSTRACT
Solving partial differential equations (PDEs) is a fundamental problem in science and engineering. While neural PDE solvers can be more efficient than established numerical solvers, they often require large amounts of training data that is costly to obtain. Active learning (AL) could help surrogate models reach the same accuracy with smaller training sets by querying classical solvers with more informative initial conditions and PDE parameters. While AL is more common in other domains, it has yet to be studied extensively for neural PDE solvers. To bridge this gap, we introduce AL.4PD,, modular and extensible active learning benchmark. It provides multiple parametric PDEs and state-of seree-of-the--ee the-art surrogate models for the solver-in-the-looe setting, enabling the evaluation existing and the development of new AL methods for neural PDE solving. We use the benchmark to evaluate batch active learning algorithms such as uncertainty- and feature- based methods. We show that AL reduces the average error by up to 71% compared random sampling and significantly reduces worst-case errors. Moreover, AL generates similar datasets across repeated runs, with consistent distributions parameters and initial conditions. The acquired datasets are reusable, providing benefits for surrogate models not involved in the data generation.
the PDE
INTRODUCTION
Partial differential equations describe numerous physical phenomena such as fluid dynamics, heat flow, and cell growth. Because of the difficulty of obtaining exact solutions for PDEs, it is common to utilize numerical schemes to obtain approximate solutions. However, numerical solvers require a high temporal and spatial resolution to obtain sufficiently accurate numerical solutions, leading to high computational costs. This issue is further exacerbated in settings like parameter studies, inverse problems, design optimization, where many iterations of simulations must be conducted. Thus, can be beneficial to replace the numerical simulator with a surrogate model by training neural network predict the simulator outputs (Takamoto al., 2022; Lippe et al., 2023; Brandstetter et al., 2021; Gupta & Brandstetter, 2023; Li et al., 2021b). In addition to being more efficient, neural surrogate models have other advantages, such as being end-to-end differentiable.
One of the main challenges of neural PDE surrogates is that their training data is often obtained from the same expensive simulators they are intended ultimately replace. Hence, training surrogate provides computational advantage only if the generation of the training data set requires fewer simulations than will be saved during inference. Moreover, it is non non-trivial to obtain training data for diverse set of initial conditions and PDE parameters required to train a surrogate with sufficient generalizability. For instance, contrary to training foundation models for text and images, foundation models for solving PDEs require targeted and expensive data generation generalize well.
Active learning a possible solution to these challenges as might help to iteratively select a smaller number of the most informative and diverse training trajectories, thereby reducing the total number of simulations required to reach the same level of accuracy. Furthermore, AL may also improve the reliability of the surrogate models by covering challenging dynamical regimes with enough training data, which may otherwise be hard to find through hand-tume -unedd input distributions. However, generating data for neural PDE solvers is challenging problem for AL due the complex regression

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Figure An extensible benchmark framework for pool-based active learning for neural PDE solvers.
tasks characterized by high-dimensional input and output spaces and time series data. While AL has been used extensively for other scientific ML domains such as material science (Lookman et al., 2019; Wang et al., 2022; Zaverkin et al., 2022; 2024), it has only been recently applied to PDEs in the context of physics-informed neural networks (PINNs; Wu et 2023a: Sahli Costabal et al. 2020; Aikawa et al. 2023), specific PDE domains (Pestourie et al., 2021; 2023), or direct prediction models (Li et al., 2024a; 2021b). Hence, AL is still unexplored for a broader class of neural PDE solvers, which currently rely extensive, brute-force numerical simulations to generate sufficient amount of training data.
This paper presents AL4PDE, the first AL framework for neural PDE solvers. The benchmark supports the study of existing AL algorithms scientific ML applications and facilitates the development of novel PDE-specific AL methods. In addition to various AL algorithms, the benchmark provides differentiable numerical simulators for multiple PDEs, such as compressible Navier-Stokes, and neural surrogate models, such the U-Net (Ronneberger et al., 2015; Gupta & Brandstetter, 2023). The benchmark is extensible, allowing new algorithms, models, and tasks added. Using the benchmark, we conducted several experiments exploring the behavior of AL algorithms for PDE solving. These experiments show that AL can increase data efficiency and especially reduce worst-case errors. Among the methods, largest cluster maximum distance (LCMD, Holzmüller et al., 2023), stochastic batch active learning (SBAL, Kirsch et al., 2023) and batch active learning via information matrices (BAIT, Ash et al., 2021) are the best-performing algorithms. We demonstrate that using AL can result in more accurate surrogate models trained in less time. Additionally, the generated data distribution is consistent between random repetitions, initial datasets, and models, showing that AL reliably generate reusable datasets for neural PDE solvers that were not used to gather the data. The code is available at https://ithuu..com comm/mmsscaamppalppp..
Contributions.
BACKGROUND
We seek the solution u [0, X R.N. of PDE with a D-dimensional spatial domain X, [1 T2, ..... T2,.........
..p
X, temporal domain t E [O, and N₂ field variables or channels c
(Brandstetter et al., 2021):
0 u (A,t, u, Ox 0xx u....).
(t. [0, T X
(1)
x) = (x), x xEX X;
xtu = 0, (t,x) E [0, x ox
(2)
Here, the boundary condition B (Eq. 2) determines the behavior of the solution at the boundaries 0. of the spatial domain X', and the initial condition (IC) u2 defines the initial state the system (Eq. 2). The vector À (A1,.., T R' with X; [a,, by] denotes the PDE parameters which influence the dynamics of the physical system governed by the PDE such as the diffusion coefficient in Burgers' equation. The field variables refer to the different physical quantities modeled in the
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solution, e.g. the density and pressure fields in fluid dynamics. In the following, we only consider single boundary condition for simplicity, and thus, a single initial value problem can be identified by the tuple 25 (u", A). The inputs to the initial value problem drawn from the test input distribution pr. ab ~ (+( = pr(uu) pr (2). The distributions are typically only given implicitly, i.e,, we are given an IC generator pr (u") and PDE parameter generator pr (2), from which we can draw samples. For instance, the ICs may be drawn from a superposition of sinusoidal functions with random amplitudes and phases (Takamoto al., 2022), while the PDE parameters (A:) are typically drawn uniformly from their interval [a:, by].
The ground truth data is generated using a numerical solver, which can be defined as a forward operator G : U x R' - ld mapping the solution at the current timestep to the one at the next (Li et al., 2021b; Takamoto et al., 2022), unt + A = ((((t,)), A) with timestep size At. Here, 2 is suitable space of functions utt,') ). The solution u is uniformly discretized across the spatial dimensions, yielding N₂ spatial points in total and the temporal dimension into N, timesteps. The forward operator is applied autoregressively, i.e., feeding the output state back into G (also called rollout), to obtain a full trajectory u = (u", u² v We aim to replace the numerical solver with neural PDE solver. While there also other paradigms such PINNs (Raissi et al., 2019), restrict ourselves to autoregressive solvers Go with ú(t + At, :) = Go (ū(t.) )). A). The training set for the said (u((,)) )) A) consists of aligned pairs of ub and the corresponding solutions obtained from the numerical solver, i.e., Swain = {(bb, ut),..., (1) No ). The neural network parameters are minimized using root mean squared error (RMSE) on training samples,
N:
1
CRMSE (u, ú)=
x,) últ, 11)
(3)
N,
where û denotes the estimated solutions of the neural surrogate models.
RELATED WORK
Neural surrogate models for solving parametric PDEs are a popular area of research (Takamoto et al., 2023; Kapoor et al., 2023; Lippe et al., 2023; Cho et al., 2024). Most existing works, however, often focus on single or uniformly sampled parameter values for the PDE coefficients and improving the neural architectures to boost the accuracy. In the context of neural PDE solvers, AL has primarily been applied select the collocation points of PINNs. A typical approach is to sample the collocation points based on the residual error directly (Arthurs & King, 2021; Gao & Wang, 2023; Mao & Meng. 2023; Wu et al., 2023a). While this strategy can be effective, it differs from standard AL since uses the "label", i.e., the residual loss, when selecting data points. In this line of work, Bruna et al. (2024) use AL to select collocation points for Neural Galerkin Schemes. Aikawa et al. (2023) use a Bayesian PINN to select points based on uncertainty, whereas Sahli Costabal et al. (2020) employ PINN ensemble for AL of cardiac activation mapping.
Pesourie et al. (2020) use AI. to approximate Maxwell equations using ensemble-based uncertainty quantification for metamaterial design. Uinertainty-basee AL was also employed for diffusion, reaction-diffusion, and electromagnetic scattering (Pestouric et al., 2023). In multi-fidelity AL, the optimal spatial resolution of the simulation is chosen (Li et al., 2020; 2021a; Wu et al., 2023b). For instance, Li et al. (2024a) use an ensemble of FNOs in the single prediction setting. Wu et al. (2023c) apply AL to stochastic simulations using a spatio-temporal neural process. Bajracharya et al. (2024) investigate AL to predict the stationary solution of a diffusion problem. They consider AL using two different uncertainty estimation techniques and selecting based on the diversity in the input space. Pickering al. (2022) use AL to find extreme events using ensembles of DeepONets (Lu et al., 2021). Gajjar et al. (2022) provide theoretical results for AL of PDEs for single neuron models. Closely related to AL the field of design of experiments (DoF, Garud et al., 2017: Qu, 2023; Huan et al., 2024) which has also been applied to neural PDE solvers (Wu et al., 2023a; Li et al., 2024b). For example, space-filling, static DoE methods such as Latin Hypercube sampling (McKay et al., 1979) can be applied to avoid clustering induced by pure random sampling of the PDE input parameters (Wu et al., 2023a; Li et al., 2024b; Chandra et al., 2024). Next to using AL, it is also possible to reduce the data generation time using Krylov subspace recycling (Wang et al., 2023) or by applying data augmentation techniques such as Lie-point symmetries (Brandstetter et al., 2022). Such symmetries could also be combined with AL using LADA (Kim al., 2021).
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Model
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Figure 2: Structural overview of the AL4PDE benchmark.
In recent years, several benchmarks for neural PDE solvers have been published (Takamoto et al., 2022; Gupta & Brandstetter, 2023; Zhongkai et al.. 2025; Luo et al., 2023; Liu et al., 2024). For instance, PDEBench (Takamoto et al., 2022) and PDEArena (Gupta & Brandstetter. 2023) provide efficient implementations of numerical solvers for multiple hydrodynamic PDEs such as Advection, Navier-Stokes, as well recent implementations of neural PDE solvers (e.g.. DilResNet, U-Net, FNO) for standard and conditioned PDE solving. Similarly, CODBench (Burark et al., 2024) compares the performance of different neural operators. WaveBench (Liu et al., 2024) is benchmark specifically aimed at wave propagation PDEs that are categorized into time-harmonic and time-varying problems. For more detailed discussion of related benchmarks, see Appendix A.3. Contrary to prior work, AL4PDE is the first framework for evaluating and developing AL methods for neural PDE solvers.
4 AL4PDE: AN AL FRAMEWORK FOR NEURAL PDE SOLVERS
The AL4PDE benchmark consists of three major parts: (1) AL algorithms, (2) surrogate models, and (3) PDEs and the corresponding simulators. It follows modular design to make the addition of new approaches or problems as easy as possible (Fig. 2). The following sections describe the AL approaches, including the general problem setup, acquisition and similarity functions, and batch selection strategies. Moreover, we describe the included PDEs and surrogate models.
4.1 PROBLEM DEFINITION AND SETUP
AL aims select the most informative training samples so that the model can reach the same generalization eтrоr with fewer calls to the numerical solver. We measure the error using test trajectories on random samples from an input distribution pr. Fig. shows the full AL cycle. Since it requires retraining the NN(s) after each round, Specifically, in each round, a batch of simulator inputs Spatch = { , L6 Nuva, } is selected. is then passed to the numerical solver, which computes the output trajectories using numerical approximation schemes. The new trajectories are then added to the training set, and the cycle is repeated.
batch AL with sufficiently large batches.
We implement pool-based active learning methods, which select from of possible inputs Spool = [ $ N called "pool". The selected batch then removed from the pool, simulated, and added to the training set Smain
Speot + Spoot \ Shatch
Smin f- Smin U sol solve(SSanah
(4)
We sample the pool set randomly from proposal distribution π. In our experiments, we sample pool and test set from the same input distribution practice. Following common practice, the initial batch is selected randomly. Besides pool-based methods, our framework is also compatible with query-synthesis AL methods that are not restricted to finite pool set. Several principles are useful for the design of AL methods (Wu, 2018): First, they should select highly informative samples that allow the model to reduce its uncertainty. Second, selecting inputs that are representative of the test input distribution at test time is often desirable. Third, the batch should be diverse, i.e., the individual samples should provide non-redundant information. The last point is particular to the batch setting, which is essential to maintain acceptable runtimes. In the following, will investigate batch AL methods that first extract latent features direct
Pr, although pr might not always be known
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uncertainty estimates from the neural surrogate model for each sample in the pool and subsequently apply a selection method to construct the batch.
4.2 UNCERTAINTIES AND FEATURES
Since neural PDE solvers provide high aigh-dimensional autoregressive rollouts without direct uncertainty predictions, many AL methods cannot be applied straighfforwardl.. In the AL4PDE framework, we select the following two different classes of methods: the uncertainty-bast approach, which directly assigns an uncertainty score to each candidate, and the reature-based framework of Holzmüller et al. (2023), which uses features (or kernels) to evaluate the similarity between inputs.
Uncertainties. Epistemic uncertainty often used measure of sample informativeness. While a more costly Bayesian approach is possible, we adopt the query-by-commiing (QbC) approach (Seung al., 1992), simple but effective method that utilizes the variance between the ensemble members' outputs as an uncertainty estimate:
N
(qbc((b))
i (ty, xxk - (ty, xx)1
(5)
N
m
m=1
Here, is the mean prediction all Nm models with (.(l, Em 0i.m (1, X)/ The ensemble members produce different outputs û, due to the inherent randomness resulting from the weight initialization and stochastic gradient descent. The assumption of QbC is that the variance of the ensemble member predictions correlates positively with the error. A high variance, therefore, points to a region of the input space where we need more data. Using the variance of the model outputs directly corresponds to minimizing the expected MSE. Note that many more error metrics can be considered for PDEs (Takamoto et al., 2022), for which measures other than the variance may be more appropriate.
Features. Many deep batch AL methods rely on some feature representation o(1b) ) E IR of inputs and utilize distance metric in the feature space as a proxy for the similarity between inputs, which can help to ensure diversity of the selected batch. A typical representation is the inputs to the last neural network layer, but other representations are possible (Holzmüller et al., 2023). For neural PDE solvers, we compute the trajectory and concatenate the last-layer features at each timestep. Since this can result in very bigh-dimensional feature vectors, we follow Holzmüller et al. (2023) and apply Gaussian sketching. Specifically, we use @sketch (x)) == UO($)/ TRP'" to reduce the feature space to a fixed dimension p' using a random matrix U E IR' x with i.i.d. standard Gaussian entries.
While ensemble- based AL methods can also be formulated in terms of feature maps (Kirsch, 2023), the use of latent features allows AL methods to work with a single model. Moreover, methods based distances of latent features can naturally incorporate diversity into batch AL by avoiding the selection of highly similar examples, Feature-based AL methods are, however, not translation- invariant. In the considered settings with periodic boundary conditions, an IC translated along the spatial axis will produce a trajectory shifted by the same amount. By using periodic padding within the convolutional layers of U-Net, the network is equivariant w.r.t. translations; hence, adding a translated version of the same IC is redundant. Uncertainty based approaches based on ensembles are mranllaion-invariin since ensemble model outputs are shifted by the same amount and produce the same outputs. To make feature-based AL translation invariant, we take the spatial average over the features.
4.3 BATCH SELECTION STRATEGIES
Given uncertainties or features, we need define method to select a batch of pool samples. As a generic bascline, we compare to the selection of (uniformly) random sampling of the inputs according to the input distribution, s/b ~ pbb)). Additionally, we include Latin Hypercube Sampling (LHS) as a static DoE baseline (McKay et al., 1979).
Uncertainty- based selection strategies. When given a single-sample acquisition function a such as the ensemble uncertainty, simple and common approach to selecting batch of k samples is
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Top-K, taking the lc most uncertain samples. However, this does not ensure that the selected batch is diverse. To improve diversity, Kirsch et al. (2023) proposed stochastic batch active learning (SBAL). SBAL samples inputs from the remaining pool set Spoot without replacement according to the probability distribution Ppower(3)) Xx a(}))", where m a hyperparameter controlling the sharpness of the distribution. Random sampling corresponds m 0 and Top-K to m The advantage of SBAL is that it selects samples from input regions that are not from the highest mode of the uncertainty distribution and encourages diversity.
reature-based selection strategies. In the simpler version of their Core-Set algorithm, Sener & Savarese (2018) iteratively select the input from the remaining pool with the highest distance to the closest selected or labeled point. While Core-Set produces batches of diverse and informative samples, its objective is to cover the feature space uniformly. Hence, Core-Set, in general, does not select samples that are representative of the proposal distribution. To alleviate this issue, Holzmüller et al. (2023) propose to replace the greedy Core-Set with LCMD, a similarly efficient method inspired by k-medoids clustering. LCMD interprets previously selected inputs cluster centers, assigns all remaining pool points to their closest center, selects the cluster with the largest sum of squared distances to the center, and from this cluster selects the point that is furthest away from the center. The newly selected point then becomes a new center and the process is repeated until a batch of the desired size is obtained. While finding efficient Bayesian AL methods for setting with high-dimensional outputs and autoregressive generation is challenging, we can apply efficient Bayesian AL methods to the proxy task of single-output Bayesian linear regression on given features. In particular, BAIT (Ash et al., 2021) aims to minimize the average posterior predictive variance (Holzmüller et al., 2023). We apply BAIT to the same aggregated features as LCMD and Core-Set.
4.4 PDEs
KS
LP
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T s
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(N...
Burgers
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256)
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2D CNS
(21, 128. 128)
(21, 64, 64)
3D CNS
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(21, 32, 32, 32)
Figure 3: Example trajectories of the PDEs.
Table I: Discretizations of the PDEs.
We consider 1D, 2D, and 3D parametric PDEs with periodic boundary conditions except for ID CNS. Table 1 lists the selected resolutions. The first ID PDE is the Burgers' equation from PDEBench (Takamoto et al., 2022) with kinematic viscosity v: 0u + vd,u (V/T)0za
Secondly, the Kuramoto-So Prramoto--Svvshiiskyy (KS) equation, deu ud,u u + Ô virrzz u = 0, from Lippe et al. (2023) demonstrates diverse dynamical behaviors, from fixed points and periodic limit cycles chaos (Hyman & Nicolaenko, 1986). Next to the viscosity v, the domain length L is also varied. Thirdly, to test a multiphysics problem with more parameters, we include the so-called combined equation (CE) from Brandstetter et al. (2021) where we set the forcing term 5
0,u + 0, (au² 38,u u + 702. u) = 0. Depending on the value of the PDE coefficients (a, B, 7), this equation recovers the Heat, Burgers, or the Korteweg de- Vries PDE. Finally, we use the com- pressible Navier-Stokes (CNS) equations in ID, 2D, and 3D from PDEBench (Takamoto et al., 2022), Oept +V (pv) 0, p(a,v + Vv) -pp
(S ///3 vv),
0.(c pv² /22 V. [(p+e pe? /2)v v v 0. The ICs generated from random initial fields. For 1D CNS, we consider an out-going boundary condition. Full details on PDEs, ICs, and the PDE parameter distributions can be found in Appendix B.
4.5 NEURAL SURROGATE MODELS
Currently, the benchmark includes the following neural PDE solvers: (i) a recent version of U- Net (Ronneberger et al., 2015) from Gupta & Brandstetter (2023), (ii) SineNet (Zhang et al., 2024), which is an enhancement of the U-Net model that corrects the feature misalignment issue in the

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Figure 4: Error the number of trajectories in the training set (N). The shaded represents the 95% confidence interval the mean calculated over multiple seeds. AL can reduce the error relative to random sampling of the inputs all tested PDEs but CNS, where the difference was not significant.
residual connections modern U-Nets and can be considered a model with saat-off-the-n accuracy, specifically for advection-type equations, and (iii) the Fourier neural operator (FNO, Li et al., 2021b).
SELECTION OF EXPERIMENTS
We investigate (i) the impact of AL methods on the average error, (ii) the error distribution, (iii) the variance and reusability of the generated data, (iv) the temporal advantage of AL, and (v) conduct an ablation study concerning the different design choices of SBAL and L.MD. We use smaller version of the modern U-Net from Gupta Brandstetter (2023). We train the model on sub-trajectories (two steps) to strike balance between learning auto-regressive rollouts and fast training. For ID CNS, found a trajectory length of four to be necessary for stable training. The training is performed for 500 epochs with cosine schedule, which reduces the learning rate from 10 10-5 The batch size is set to 512 (2D CNS: 64). We use an exponential data schedule, i.e., in each AL iteration, the amount of data added equal to the current training size (Kirsch et al., 2023). For 1D equations, with 256 trajectories. The pool size fixed to 100,000 candidates (3D: 30000). The uncertainty is estimated using two ensemble members (for fair comparison, just the first model of the ensemble is used to measure the error). For Burgers, we choose the parameter space v [0.001. 1) and sample values uniformly at random but on logarithmic scale. For the KS equation, besides the viscosity E [0.5, 4), we vary the domain length L E [0.1, 100) the second parameter. For CE, the parameter space is defined be a E (0,3), 3 E [0, 0.4). 7 E [0, 1). For the ID and 2D CNS equations, we set 7,, < E [10-4, 10-1) and draw values on logarithmic scale with the Burgers' PDE. Additionally, we use random Mach numbers m E [0.1. for the IC generator. We repeat the experiments with five random seeds (Burgers: ten, 3D CNS: 3) and report the 95% confidence interval of the mean unless stated otherwise. The test set consists of 2048 trajectories simulated with random inputs drawn from (16). Due to the memory and compute-intensine nature of 3D>
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Figure 5: Error quantiles over the number of trajectories in the training set (N). The 50%, 95%, and 99% quantiles are displayed using full, dashed, and dotted lines, respectively. AL especially improves the higher error quantiles, making the trained model more reliable.
time-dependent PDEs, we had to use smaller train and test sets, as well as choose different model and training parameters and use conditional version of the 3D FNO model (see Appendix C).
Comparison of AL methods. Figure 4 shows the RMSE for the various AL methods and PDEs. AL often reduces the eтror compared to sampling uniformly random for the same amount of data. The advantage of AL is especially large for CE, which is likely due to the diverse dynamic regimes found in the PDE. SBAL, BAIT, and LCMD achieve similar errors on all PDEs with the exception of KS, where only SBAL and BAIT can improve over random sampling. AL can reach lower error values with only a quarter of the data points in the case of CE and Burgers. However, the greedy methods Top-K and Core-Set even increase the error for some PDEs. We did not find a notable difference between the static DoE method LHS and random sampling. The difference in the CNS tasks was significant, likely due to the performance of the base model training (see Fig, 8a) for stronger model). Worst-case errors are of special interest when solving PDEs. Since we found the absolute maximum algorithms reduce the higher quantiles while the 50% percentile error is increased in some cases.
be unstable, we show the RMSE quantiles in Figure 5. Notably, all AL
Different Error Functions. It is important consider error metrics for surrogate model training besides the RMSE (Takamoto et al., 2022). Thus, we explore the impact of AL on the mean absolute error (MAE) an example of an alternative metrie. As depicted in Figure 7a, SBAL, when using the absolute difference between the models as the uncertainty, can also successfully reduce the MAE, However, the MAE does not improve greatly relative to random sampling when the standard variance between the models is used. Hence, it is crucial to tailor the AI. method to the relevant metric.
Generated Datasets. The marginal distributions of the PDE and the IC generator parameters implicitly sampled from by AL are shown in Figure 6 for CE. These distributions highly similar for different random seeds, and thus, AL reliably selects similar training datasets. The various AL methods generally sample similar parameter values but differ substantially in certain regions of
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Figure 6: Marginal distribution of the PDE parameters (a, 3, 7) for CE and the amplitudes of the IC in the training set generated by AL for CE (relative to the uniform distribution). The shaded area represents the standard deviation between the random seeds. All AL methods exhibit small standard deviation, indicating that they reliably generate similar datasets between independent runs.
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Figure 7: (a) AL with the MAE as the objective on Burgers, compared to the MAE of the same setup trained with the RMSE (dashed). Considering the desired error metric in the uncertainty estimate and training loss is essential. (b) Error of the standard U-Net on Burgers, with data generated using FNO U-Net with SBAL. The selected data is also helpful for model not used during AL. (c) Error of the standard U-Net on Burgers over the required total time. Using smaller FNOs to select the data, SBAL can provide smaller errors in the same amount of time.
the parameter space (Appendix G.3). In general. the methods appear to sample more in the region of the chaotic KdV equation (a = 3, 3 0 1). Appendix G provides the distributions for all PDEs and visual examples. To investigate the effect of the generated data other models, we use an FNO ensemble to select the data that we use train the standard U-Net. Figure 7b depicts the error of the U-Net over the number of samples selected using the FNO ensemble, showing the selected data is beneficial for models not used for the AL-based data selection. The reusability of the data especially important since, otherwise, the whole AL procedure would have to be repeated every time a new model is developed.
Temporal Behavior. The main experiments only provide the error over the number of data points since we use problems with rather fast solvers to accelerate the benchmarking of the AL methods. Additionally, more lightweight model, trained for shorter time, might be enough for data selection even if it does not reach the best possible accuracy. To investigate AL in terms of time efficiency gains, we perform one experiment on the Burgers' PDE, for which the numerical solver the most expensive among all 1D PDEs due to its higher resolution. We use SBAL with an ensemble of smaller FNOs (See Appendix C.6 for more details). We train regular U-Net on the AL collected data, which allows us use small, lightweight model for data selection only and an expensive one to evaluate the data selected. Figure 7c shows the accuracy of the evaluation U-Net over the cumulative time consumed for training the selection model, selecting the inputs, and simulation. For the random baseline, only the simulation time is considered. On Burgers, AL provides better accuracy for the same time budget.

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(a) Model Architecture
Ablations
(c) LCMD Features
U-Net SineNet
FNO
M 2
PINO Random
LL
ML
ML
LL
RMSE
0.01
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Figure 8: (a) Different base models on 2D CNS using SBAL (solid) and random sampling (dashed). SBAL can also improve the accuracy of other models besides the U-Net. (b) Ablation study on SBAL (Burgers' equation). SBAL already works reliably with only M = 2 models in the ensemble. Using the PINO loss (Li al., 2024c) instead of the ensemble uncertainty does not provide a meaningful uncertainty, as shown by the error on par with random sampling. (c) Comparison of different feature vectors for LCMD well as the features of the mid layer (ML) and its spatial average (ML). Averaging the feature maps improves the error, indicating the importance of considering the model invariances.
CF. Shown are the last layer feature map (LL., its spatial average (LL.), as
Ablations. We ablate different design choices for the considered AL algorithms. For the SBAL algorithm, we investigate the base model architecture (Fig. 8a) and the ensemble size (Fig. 8b). On 2D CNS, the accuracy of both SineNet and FNO can be significantly improved using SBAL, showing that AL is also helpful for other architectures. The improvement is even clearer than with the U-Net, which did not show a statistically significant advantage. Consistent with prior work (Pickering et al., 2022), choosing an ensemble size of two models is already sufficient (Fig. 8b). In general, the average uncertainty and eтror of a trajectory with two ensemble members are correlated with a Pearson coefficient of 0.41 on CE in the worst case up to 0.94 2D CNS (Table 10). Adaptive sampling methods utilized in the field of PINNs (Gao & Wang, 2023; Wu et al., 2023a), select collocation points based on the PDE loss. While this is not directly transferable our setting (Section 3), try to use the PINO loss (Li et al., 2024c) as an uncertainty estimate in combination with SBAL. As shown in Figure 8b, this is not an effective selection criterion for autoregressive neural PDE solvers. Figure 8c) compares different feature choices for the LCMD algorithm, which are used to calculate the distances. Using the spatial average of the last layer features produces higher accuracy than using the full feature vector or the features from the bottleneck step in the middle of the U-Net. Thus, it indeed important for distance-based selection to consider the equivariances of the problem in the distance function.
CONCLUSION
This paper introduces ALAPDE, an extensible framework to develop and evaluate AL algorithms for neural PDE solvers. AL4PDE includes diverse set of PDEs in ID, 2D, and 3D spatial dimensions, surrogate models including U-Net, FNO, and SineNet, and AL algorithms such as SBAL and LCMD. An initial study shows that existing AL algorithms can already be advantageous for neural PDE solvers and can allow a model to reach the same accuracy with up to four times fewer data points. Thus, our work shows the potential of AL for making neural PDE solvers more data-efficient and reliable for future application cases. However, the experiments also showed that stable model training can be difficult depending on the base architecture (2D CNS). Such issues especially impact AL since the model is trained repeatedly with different data sets, and the data selection relies on the model. the surrogate model training is necessary. Another general
Hence, more work on the reliability open issue of AL is the question of how to select hyperparameters that work sufficiently well on the growing, unseen datasets during AL. To be closer to realistic engineering applications, future work should also consider more complex geometries and boundary conditions, well irregular grids. AL could be especially helpful in such settings due to the inherently more complex input space from which to select.
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REPRODUCIBILITY STATEMENT
The code is available at https:/githhb.ccm httpg/hhi..ooommmussaaapp//////p
The repository contains the full configuration files of all reported experiments. Appendix B and C describe the main experimental as well as the model details. For reliable results, we repeat all experiments with ten seeds (Burgers), five seeds (KS, CE, and CNS), and three seeds (3D CNS) and report the 95% confidence interval of the mean unless stated otherwise.
ACKNOWLEDGEMENTS
We thank the anonymous reviewers on OpenReview whose questions were helpful in improving the manuscript. We acknowledge the support of the German Federal Ministry of Education and Research (BMBF) as part of InnoPhase (funding code: 02NUK078). Marimuthu Kalimuthu and Mathias Niepert are funded by Deutsche Forschungssemmiiscchaf (DFG, German Research Foundation) under Germany's ' Excellence Strategy EXC 2075 390740016. We acknowledge the support of the Stuttgart Center for Simulation Science (SimTech). The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Daniel Musekump, Marimuthu Kalimuthu, and Mathias Niepert. Moreover, the authors gratefully acknowledge the computing time provided to them at the NHR Center NHR4CES at RWTH Aachen University (project number p0021 158). This is funded by the Federal Ministry of Education and Research, and the state governments participating on the basis of the resolutions of the GWK for national high-performance computing at universities (http://wwww.n www.wrr-eeeeii.eee... www.rreereeii.enuneeeeeeeeeeeeee www.dd/eeeeeaaaateeeeeer
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ACTIVE LEARNING FOR NEURAL PDE SOLVERS SUPPLEMENTAL MATERIAL
A Additional Background on Related Work
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A.I General Active Learning
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A.2 Uncertainty Quantification (UQ)
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A.3 Further Scientific Machine Learning Benchmarks
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B Additional Problem Details
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B.1 Burgers' Equation
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B.2
Kuramoto-Sivashinsky (KS)
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B.3 Combined Equation (CE).
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B.4 Compressible Navier-Stokes (CNS)
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Additional Model and Training Details
C.I Fourier Neural Operators (FNOs)
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C.2 U-shaped Networks (U-Nets)
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C.3 SineNet
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C.4 Hyperparameters and Training Protocols
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C.5 Hardware and Runtime
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C.6 Timing Experiment
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D Framework Overview
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Additional Experiments
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Detailed Results
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G Overview of the Generated Datasets
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Example Trajectories
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G.2 IC Parameter Marginal Distributions
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G.3 PDE Parameter Marginal Distributions
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A ADDITIONAL BACKGROUND ON RELATED WORK
In this section, we elaborate on related works that tackle active learning in relevant settings and problems discussed here. Moreover, we summarize related work on uncertainty quantification and SciMI. benchmarks closely related to the proposed AL4PDE benchmark.
A.1 GENERAL ACTIVE LEARNING
Most AL algorithms are evaluated on classic image classification datasets (Ash et al., 2021; 2019) and many benchmarks also consider the more common classification setting (Rauch al., 2023; Yang & Loog, 2018; Zhan et al., 2021). There is also work on specialized tasks such as entity matching (Meduri et al., 2020), structural integrity (Moustapha et al., 2022), material science (Wang et al., 2022), or drug discovery (Mehrjou et al., 2021). Holzmüller et al. (2023) present benchmark for AL of single-output, tabular regression tasks. Wu et al. (2023a) study different adaptive and non-adaptive methods for selecting collocation points for PINNs. Ren al. (2023) benchmark pool-based AL methods on simulated, mostly tabular regression tasks.
Related to AL is the field of design of experiments (DoE, Garud et al., 2017; Qu, 2023; Huan et al., 2024). In static DoE, a set of inputs is selected without using feedback from the investigated process. Space-filling DoE methods try cover the input space optimally without considering any model assumption or feedback (Huan et al., 2024). A pure random sampling of the input space may lead to undesirable clustering of samples, leading redundant information. For example, Latin Hypercube sampling (McKay et al., 1979) divides each input variable into equally-spaced intervals, takes one sample from the interval, and then mixes the samples from the different variables. In D-optimal experimental design, the next sample is selected such that the uncertainty in the parameters of a linear regression model is reduced by minimizing the determinant of its Fisher information matrix (Wald, 1943; Kiefer, 1958; Huan et al., 2024). Most similar to AL is sequential DoE. For instance, in scquential Bayesian optimal design, the prior updated iteratively to the posterior over the model parameters after including the new measurements (Huan et al., 2024). Based on the posterior, a criterion such as the expected information gain (Lindley, 1956; Huan et al., 2024) can be utilized to select the optimal next sample. While such methods have strong theoretical underpinning, they have been developed for linear regression models and, hence, are not directly applicable to neural networks.
Bayesian linear regression model is
In terms of deep active learning methods for regression, there are multiple approaches: Query-by- committee (Seung et al., 1992) uses ensemble prediction variances as uncertainties. Tsymbalov et al. (2018) use Monte Carlo dropout to obtain uncertainties; however, their method is only applicable by training with dropout. Approaches based on last-layer Bayesian linear regression (Pinsler et al., 2019; Ash et al., 2021) are often convenient since they do not require ensembles or dropout. These methods are applicable in principle in our setting but lose their original Bayesian interpretation since the last layer of a neural operator is applied multiple times during the autoregressive rollout. Distance-based methods like Core-Set (Sener Savarese, 2018; Geifman & El-iani,, 2017) and the clustering- LCMD (Holzmüller et al., 2023) exhibit better runtime complexity than last-layer Bayesian methods while sharing their other advantages (Holzmüller et al., 2023). Since these algorithms just require some distance function between two input points, in Section 4.2.
adapt them the neural PDE solver setting
A.2 UNCERTAINTY QUANTIFICATION (UQ)
Uncertainty quantification has been studied in the context of SciML simulations. Psaros et al. (2023) provide a detailed overview of UQ methods in SciML, specifically for PINNs and DeepONets. However, effective and reliable UQ methods for neural operators (i.e., mapping between function spaces) and high dimensionality of data, which is common in PDE solving, remain challenging.
Neural PDE Solvers. LE-PDE-UO UQ (Wu al., 2024) deals with a method to estimate the uncertainty of neural operators by modeling the dynamics in the latent space. The model has been shown to outperform other UQ approaches, such as Bayes layer, Dropout, and L2 regularization on Navier- Stokes turbulent flow prediction tasks. Unlike the considered setting in our case, the model utilizes
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history of 10 timesteps and has been tested only fixed PDE parameter. Hence, it is unclear whether the robustness of this approach remains when these settings change.
Mouli et al. (2024) aim to develop cost-efficient method for uncertainty quantification of parametric PDEs, specifically one that works well in the out-of-domain test settings of PDE parameters. First, the study shows the challenges of existing UQ methods, such as the Bayesian neural operator (BayesianNO) for out-of-domain test data. It then shows that ensembling several neural operators is an effective strategy for UQ that is well-correlated with prediction errors and proposes diverse neural operators (DiverseNO) as cost-effective way to estimate uncertainty with just single model based on FNO outputting multiple predictions.
Thakur (2024) studies UQ in the context of neural operators and develops probabilistic FNO model quantify alcatoric and epistemic uncertainties. Weber al. (2024) study UQ for FNO and propose a Laplace approximation for the Fourier layer to effectively compute uncertainty.
A.3 FURTHER SCIENTIFIC MACHINE LEARNING BENCHMARKS
In recent years, various benchmarks and datasets for SciML have been published. We outline some of the major open-source benchmarks below.
PDEBench (Takamoto et al., 2022) is a large-scale SciML benchmark of ID to 3D PDE equations modeling hydrodynamics ranging from Burgers' to compressible and incompressible Navier-Stoke Stokes equations. PDEArena (Gupta & Brandstetter, 2023) a modern surrogate modeling benchmark in- cluding PDEs such incompressible Navier-Stokes, Shallow Water, and Maxwell equations (Brand- stetter et al., 2023). CFDBench (Luo et al., 2023) is a recent benchmark comprising four flow problems, each with three different operating parameters, the specific instantiations of which include varying boundary conditions, physical properties, and geometry of the fluid. The benchmark com- pares the generalization capabilities of range of neural operators and autoregressive models for each of the said operating parameters. LagrangeBench (Toshev et al., 2023) is large-scale benchmark suite for modeling 2D and 3D fluid mechanics problems based on the Lagrangian specification of the flow field. The benchmark provides both datasets and baseline models. For the former, it introduces seven datasets of varying Reynolds numbers by solving a weak form of NS equations using smoothed particle hydrodynamics.. For the latter, efficient JAX implementations of GNN baseline models such as Graph Network- based Simulator and (Steerable) Equivariant GNN are included. EAGLE (Janny et al., 2023) introduces industrial-grade dataset of non-steady fluid mechanics simulations encompassing 600 geometries and 1.1 million 2D meshes. In addition, to effectively process dataset of this scale, the benchmark proposes efficient multi-scale attention model, mesh transformer, to capture long-range dependencies in the simulation. BubbleMLL (Hassan et al., 2023) is thermal simulations dataset comprising boiling scenarios that exhibit multiphase and multiphysics phase change phenomena. It also consists of a benchmark validating the dataset against U-Nets and several variants of FNO.
B ADDITIONAL PROBLEM DETAILS
In the following section, we will discuss the tasks considered in detail. Table shows the temporal and spatial resolution of the considered PDEs.
B. BURGERS' EQUATION
The ID Burgers' equation is written as
O.u + ud_u (1/))0mm..
The spatial domain is set to x E [o, 1]. Following the parameter spacing of the PDE parameters values in PDEBench (Takamoto al., 2022) and CAPE (Takamoto al., 2023), we draw them logarithmic scale, i.e., we first draw As.normed uniformly from [0, 1) and then transform the parameter its domain [a., b;) using
X; = exp(log(bs/ (b////////// m..ommdd))..
We use the FDM-based based JAX simulator and the initial condition generator from PDEBench (Takamoto al., 2022). The ICs constructed based superposition of sinusoidal waves (Takamoto et al.,
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2022),
Nw
(x)
Σ
A, sinnnnkk,, L oi),
(8)
where the wave number K; is an integer sampled uniformly from (), amplitude A, is sampled uniformly from [0, 1), and phase 6; from [0, 2T)). The number of waves N₂ is set to 2. Windowing is applied afterward with a probability of 10%, where all parts of the IC are set to zero outside of
[xL, TL is drawn uniformly from [0.1, 0.45) and zR from [0.55, 0.9). Lastly, the sign of u is flipped for all entries with probability of 10%.
B.2 KURAMOTO-SIVASS
(KS)
The ID KS equation reads as
0,u + ud,u u + Ogr vd 0 x E [0,
The ICs are generated using the superposition of sinusoidal waves (Eq. (8)), but k; is sampled from [1, 10), A, from [-1,1) 1) and di from [0, 2x). No windowing or sign flips are applied. The total number of waves No. in this case is set to 10. Since we cannot omit the first part of the simulations as Lippe et al. (2023), we reduce the simulation time to 40s, but allow for more variance in the ICs to reach the chaotic behavior easier by increasing the number of wave functions of the IC. The trajectories are obtained using JAX-CFD (Dresdner et al., 2023). The PDE parameters are drawn uniformly from their range (no logarithmic scale).
B.3 COMBINED EQUATION (CE)
We adopt the combined equation albeit without the forcing term and the corresponding numerical solver from Brandstetter et al. (2021).
3au + 0g (aw² B8,u + y0
(10)
As for the IC, the domain of k; is set [1, 3) and for A, it set as 0.4, 0.4). The number of waves N.. is set to 5, and no windowing or sign flips are applied either. The PDE parameters are also drawn uniformly from their range. Depending on the choice the PDE coefficients (a,, 3, 7), this equation
recovers the Heat (0, 1, 0), Burgers (0.5, 1, 0), or the Sorteweg-de-Vries (3, O, 1) PDE. The spatial domain is set to x E [0, 16).
B.4 COMPRESSIBLE NAVIER- AAVIER-STOKES STOKES (CNS)
The CNS equations from PDEBench (Takamoto al., 2022) written
p.p + V (pv) = 0,
(11a)
p Vv) Vp n/v (/+7 v),
(1lb)
v 0.(e p /2) V +p+ /- 0,
(11c)
where o' is the viscous tensor. For 2D, the equation has four channels (density p. pressure p, velocity x-component U.. and y-component By, whereas, for 3D, we have an extra velocity-z component U= the fifth channel in addition the above. The spatial domain is set
[0, 1] x (0, 1] for 2D, and as the unit cube (J0, 1] x J0, 1] x [0. 1)) for 3D. We use the JAX simulator and IC generator from PDEBench (Takamoto et al., 2022) for CNS equations. The PDE parameters are drawn in logarithmic scale as in Eq. (7). The IC generator for the pressure, density, and velocity channels is also based on the superposition of sinusoidal functions. However, the velocity channels are renormalized so that the IC has a given input Mach number. Secondly, we constrain the density channel to be positive by
up Po + Ap u/, max() u1,())))
(12)
where Po is sampled from [0.1, 10) and A from [0.013, 0.26). The pressure channel p is similarly transformed using Ap E [0.04, 0.8). The offset Pn is defined relatively to Po po Topo with
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To E [0.1, 10). The compressibility is reduced using Helmholtz-dccompssttiee 2022). A windowing is applied with a probability of 50% to a channel. For 3D CNS, the considered domain for the PDE coefficients is 17,5 E [10-3 10 be equal and not independently drawn.
(Takamoto et al.,
For ID CNS, the PDE coefficients are set to
C ADDITIONAL MODEL AND TRAINING DETAILS
This section describes the baseline surrogate models used in more detail, lists the hyperparameters, and explains various training methods. First, we provide a short description of the base models used. Then, we explain the training methods and list the hyperparameters.
C.I FOURIER NEURAL OPERATORS (FNOS)
We the FNO (Li al., 2021b) implementation provided by PDEBench (Takamoto et al., 2022). FNOs are based on spectral convolutions, where the layer input is transformed using a Fast Fourier Transformation (FFT), multiplied in the Fourier space with weight matrix, and then transformed back using an inverse FFT. Following the recent observations made Lanthaler et al. (2023; 2024) that only small fixed number of modes are sufficient achieve the needed expressivity of FNO, we retain only limited number of low-frequency Fourier modes and discard the ones with higher frequencies. The raw PDE parameter values are appended as additional constant channels to the model input (Takamoto et al., 2023) as the conditioning factor.
C.2 U-SHAPED NETWORKS (U-NETS)
U-Net (Ronneberger et al., 2015) is common architecture in computer vision, particularly for perception and semantic segmentation tasks. The structure resembles an hourglass, where the inputs are first successively downsampled at multiple levels and then gradually, with the same number of levels, upsampled back to the original input resolution. This structure allows the model to capture and process spatial information at multiple scales and resolutions. The U-Net used this paper is based on the modern U-Net version of Gupta & Brandstetter (2023), which differs from the original U-Net (Ronneberger et al., 2015) by including improvements such as group normalization (Wu & He, 2018). The model is conditioned on the input PDE parameter values, where they transformed into vectors using a learnable Fourier embedding (Vaswani et al., 2017) and projection layer and are then added the convolutional layers' inputs in the up and down blocks.
C.3 SINENET
U-Nets were originally designed for semantic segmentation problems in medical images (Ronneberger al., 2015). Due its intrinsic capabilities for multi-scale representation modeling, U-Nets have been widely adopted by the SciML community for PDE solving (Takamoto et al., 2022; Gupta & Brandstetter, 2023; Lippe et al., 2023; Rahman et al., 2022; Ovadia et al., 2023). One the important components of U-Nets to recover high-resolution details in the upsampling path is by the fusion of feature maps using skip connections. This does not cause an issue for semantic segmentation tasks since the desired output for given image is segmentation mask. However, in the context of time-dependent PDE solving, specifically for advection-type PDEs modeling transport phenomena, this is not well-suited since there will be "lag" in the feature maps of the downsampling path since the upsampling path is expected to predict the solution u for the next timestep. This detail was overlooked in U-Net adaptations for time-dependent PDE solving. SineNet is a recently introduced image- to- D--maage model that aims to mitigate this problem by stacking several U-Nets, called waves, drastically reducing the feature misalignments. More formally. SineNet learns the mapping
xt = P((u:-A41 "...-,,,........ ..))
u+t1 = Q(x(+1)
E++A = Vi((x-Aiii):
k l=
Unlike the original SineNet, our adaptation uses only one temporal step as a context to predict the solution for the subsequent timestep.
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C.4 HYPERPARAMETERS AND TRAINING PROTOCOLS
During AL, we use m = for power sampling and prediction batch size of 200 for the pool, except for 3D CNS for which use 16 due memory limitations. The features of all inputs are projected using the sketch operator to dimension of 512. Table 2 lists the model hyperparameters.
U-Net
Activation
GELU (Hendrycks Gimpel, 2016)
Conditioning
Fourier (Vaswani et al., 2017)
Channel multiplier
[1. 2, 2, 4]
Hidden Channels
16
Params
3,378,865 (1D) 9,182,036 (2D)
FNO
Activation
GELU (Hendrycks & Gimpel, 2016) Additional input channel
Conditioning
Layers
Width
64 (1D) 32 (2D & 3D)
Modes
20
# Params
680,834 (ID) / 6,563, 10 (2D) / 262,15,,99 (3D)
SineNet
Activation
GELU (Hendrycks & Gimpel, 2016)
Conditioning
Fourier (Vaswani et al., 2017)
Hidden Channels
32
Waves Params
5,020,840 (2D)
Table 2: Model hyperparameters. For FNO ID, the parameter counts are for Burger's PDE, whereas for FNO 2D and 3D, the CNS equations of respective spatial dimensions are considered.
The inputs are channel-wise normalized using the standard deviation of the different channels on the initial data set. The outputs are denormalized accordingly. The input only consists of the current state u, not including data from prior timesteps. All models are used to predict the difference the current timestep (for U-Net, the outputs multiplied with fixed factor of 0.3 following Lippe et al. (2023)).
We employ one- and two-step training strategies during the training phase and complete rollout of the trajectories during validation. For the FNO model in the 2D and 3D experiments, we found it better add gradient clipping to prevent sudden divergence in the training curve. To account for the very the upper limit 5 times the highest gradient found
the teacher-foree foreing schedule from Takamoto et al. (2023). We found it necessary to
different gradient norms among problems, in the first five epochs. Afterward, the limit is adapted using a moving average.
We conduct experiments the -meedependent 3D spatial Navier-Stokes equations and evaluate the efficacy of active learning in this challenging setup. For 3D, we set the pool size for the active learning methods on 3D CNS 30,000 due to memory and compute time limitations. We train the ML models. 3D FNO, using teacher forcing schedule (Takamoto et al., 2023) with batch size of 10 for four active learning iterations. The initial training data consists of 64 trajectories, whereas the validation and test sets each have 512 trajectories.
C.5 HARDWARE AND RUNTIME
The experiments were performed NVIDIA GeForce RTX 4090 GPUs (one per experiment), except for the 3D CNS case, which was performed on a single 96 GB H100 GPU. Table shows the runtime and GPU memory required for the PDEs during training.
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Burgers
KS
CE
ID CNS
2D CNS
3D CNS
Runtime in
Random
15.6
13.4
16.2
19.2
37.6
9.5
SBAL
21.6
20.9
25.4
26.8
55.8
14.4
LCMD
15.1
13.7
17.0
20.3
38.2
12.4
Core-Set
14.7
13.7
16.7
20.3
39.9
12.6
Top-K
21.8
20.4
26.1
26.9
56.5
14.4
BAIT
15.6
13.8
17.3
20.5
40.0
12.9
LHS
18.0
13.4
16.8
23.7
39.8
9.6
Training Memory in GB
All
8.16
8.18
4.47
6.88
7.29
66.63
Table 3: Total runtime of the different AL methods and the memory during training (since all methods train the same model, the memory usage during training is identical).
C.6 TIMING EXPERIMENT
realistic time measurement for the simulator of Burgers' equation is challenging. Firstly, observed that we can reach the shortest time per trajectory by setting the batch size to 4096 (0.52 seconds). Therefore, we this the fixed time per trajectory. The actual simulation times per AL iteration are higher since we start with batch sizes below this saturation point. Secondly, the simulation step size is adapted the PDE parameter value due the CFL condition (Lewy et al., 1928). Therefore, would be beneficial to batch similar parameter values together and also to consider the parameter simulation costs in the acquisition function. Figure shows training, selection, and simulation times.
T Tirai
Toat
10
Time
10¹
AT. Iteratioo
Figure 9: Cumulative training, selection, and simulation times necessary to reach the given active learning iteration (e.g., time to select data for iteration 2 counted in iteration 2) for 1D Burgers PDE.
The FNO surrogate used for selection is only trained for 20 epochs with a batch size of 1024. We use one-step training, and the learning rate of 0.001 is not annealed. The model itself has width of 20 and uses 20 modes, resulting 36,706 parameters. During selection, a batch size 32,768 is used.
D FRAMEWORK OVERVIEW
The framework has three major components: Model, acts as container of all the PDE-specific information and contains the Simulator. PDEParamGenerator: and ICGenerator classes. EDEParamGenerator and TCGenerator draw samples from the test input distribution pr. The inputs are first
and Task. Task
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drawn from normalized range and then transformed into the actual inputs. Afterward, the inputs can be passed to the simulator to be evolved into trajectory. Listing shows the pseudocode of the (random) data generation pipeline. In order to implement new PDE, user has to implement a new subelass of S Simulator overwrite the _call function and, desired, add a new ICGenerator.
class PDEParamGenerator:
def hww_ooooee__deepp___e_______eeee (self, n):
Generates the random PDE parameters in normed space (e.g. between 0 and 1).
def get_pde params (self, pde_params_normed] Transforms the normed parameters to their true value.
10
class ICGenerator:
def Initializo_ie_prre (self, n):
Generates the random parameters of an IC (e.g. Mach number).
15
def generate (self, ic ic_ params, ade_params) Transforms the IC parameters and FDE parameters to the IC.
19
class Simulator:
def _call_ ic, pde_params, grid): Evolves the for a given PDE parameter.
gonorate pdc parameters pde_parame_normed wtw..ennngg_noomee__eee..... params (n)
28
par_marams pde_gen. get_pde_parame aade_parame_____eeee normed)
generate ICs
ic_params ic__en.initiale wee.iiiiiilliii
(n)
ic_gen. www.geeeneraeinieeeeeeeeee itt.eeeeoeettaaaaaaooooooooooo conditions params, poe_parana
33
trajectories = sim(ic, pde param, grid)
Listing I: Interface and example code for generating inputs and simulation.
Listing shows the interface for the Model and ProbModel classes. Model provides functions to rollout a surrogate and deals with the training and evaluation. In order to add new surrogate, user has to overwrite the forward method. The rollout function also allows to get the internal model features for distance- acquisition functions. P ProbModel is an extension of the Model class, which adds the possibility of getting an uncertainty estimate. After training the model, the BatchSel ect i on class is called in order to select new set of inputs. The most important subclass the Pool BaleBdedd class, which deals with managing the pool and provides the se cellec___eett_ next method, which new pool-based method has overwrite.
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class Model (nn. Modulo)
2
def init_training (self, al :
Reset model, optimizer, scheduler,
5
5
def fsrward(self, xx, grid, paran, return_featured features):
# Predict the next state.
def rollout (self, xx, grid, final_step, param, return_features) Autoregressive rollout of the model until timestep final_step,
10
def evaluste step, loader, prefix):
Evaluate the model the given dataset (e.g. test).
15
def train_ (solf, current_epoch, total_epoch, num_epoch) Train the model for one epoch.
def train_n_epoch (self, al_iter, num_spoch): Train the modei.
19
20
class ProbModel (Model)
24
def uncertainty(self, xx, grid, param): Get uncertainty over the next state.
def unc_roll_out (self, xx, grid, Final_step param, aetur_feettuee, Compute prediction and uncertainty the rollout.
29
class BatchSelection:
def (seer prob_model, al_iter, train_loader)
34
Select new inputs and pass them to the simulator.
class PoolBased (Batcheelectiitn)
38
def select_next step, prob_madel, ic_pool, pde_paran_pool, ic_train, aae_param_train, grid, al_iter); Select new Input from (ic_pool, pdc_paran__ool).
for al_iter in range (nun_al_itee) retrain model
aecb.modee..traiinn.eeoeeo (al_ iter, num_epoch)
47
select next inputs
batch_ael, generate (prob_model al_iter, train_loader)
Listing 2: Interface and example code for the neural operator models and AL methods.
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E ADDITIONAL EXPERIMENTS
Figure 10 shows an experiment with a smaller pool that is completely labeled after the last AL iteration. Sufficient pool size important for the AL algorithm in order be able focus on the difficult dynamical regions.
Small Pool
Random
SBAL
SBAL Large Pool
RMSE
256
512
1024
2048
4096
8192
N
Figure 10: Error the number of trajectories for experiment with a pool size of only 8192 possible inputs (including initial data) on CE. Compared to the main SBAL results with the larger pool of 100,000 samples.
F DETAILED RESULTS
Tables 4-9 list the results from the main experiments. Table 10 shows the Pearson and Spearman coefficient of the average uncertainty per trajectory with the average error per trajectory. Among the PDEs, the Pearson correlation coefficient is the lowest on CE. The Spearman coefficient, which measures the correlation in terms of the ranking, is above 0.54 on average for all experiments.
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Iteration
RMSE x x10-2
Random 3.684 + 1.203 3.278 2.100
1.607 + 0.485
1.062 + 0.614
0.552 0.133
SBAL 3.684 + 1.203 0.229
0.586 0.100
0.405
0.259
0.028
LCMD 3.684 + 1.2030.808
+ 0.0533.522.
0.052
0.044
0.269
Core-Set3.654 + 1.203 1.021
0.659 0.100
0.176
Top-K 3.684 = 1.203
0.250
0.964
0.477
0.360 0.096
BAIT 3.684 = 1.203
0.903
= 0.138
0.537 = 0.030
0.392 0.035
0.266 0.024
LHS 3.441 + 1.708
1.930 + 0.300
1.354 + 0.529
1.057
0.539
0.521 + 0.117
50% Quantile x10
Random 0.122 0.122 0.015
0.083 + 0.010
0.058 0,005 0.007
SBAL 0.182
4.178:00003.
0.105
#.011
0.078
0.006
LCMD
0.129
0.101 #.011
0.050
Core-SetO01883 0.015
0.017
0.133
0.063 0.014
Top-K 0.182 + 0.015 0.020
0.176
0.109
0.078 0.012
BAIT 0.182 0.015 0.014
0.115
0.079
0.058 0.008
LHS 0.174 0.014..116
0.014
+ 0.009
0.062 + +
0.007
0.054
95% Quantile x10-2
Random 1.468 +: 0.136 0.134:0..22
0.502 + 0.037
0.343 0.014
0.025
SBAL 1.468 + 0.136 1.054
1.44400000
0.409
0.064
0.269 + 0.026
LCMD 1.468 = 0.136
+ 0.069
0.503 0.091
0.347
0.259
0.020
Core-Set 1.468 + 0.166 0.865
0.123
0.503
0.336 0.034
Top-K 1.468 + 0.136
1.273
0.177
0.064
0.449 0.077
BAIT 1.468 = 0.136
0.800
0.532
0.045
0.378
4.274
LHS 1.390 = 0.142
0.803 + 0.114
0.474 + 0.038
0.344 + 0.027
0.246 + 0.024
99% Quantile x10-2
Random 6.315 + 3.327
1.653
0.968
0.649 0.027
SBAL 6.315 - 0.838 3.949
L.360
0.987
0.599 0.056
LCMD 6.315 + 0.838.888111 1.808.80222
0.157
1.223
0.237
0.819 0.108
0.573
0.041
Core-Set6.315 + 0.838 2.461
0.500
1.756 0.360
1.153
0.703 0.056
Top-K 6.315 = 0.838 1.688
1.089
0.129
1.048 0.326
BAIT 6.315 0.838 2.371 #
1.255
0.853 + 0.065
0.612 + 0.055
LHS 6.215 + 1.012 3.017 + 0.476
1.515
0.963 0.046
0.650 0.047
Table 4: Error metrics Burgers' equation.
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Iteration
RMSE
Random 0.452 = 0.026 0.370 0.012
0.010
0.229 0.010
SBAL 0.452 + 0.0260.347: 0.0200.288
0.281 0.010
0.008 0.200 + 0.012
LCMD 0.452 + 0.026 0.370
0.315
0.266
0.219
Core-Set 0.452 + 0.026 0.389
0.003
0.006
0.235 0.000
Top-K 0.452 + 0.026 0.378
0.305
0.225 0.014
0.005
BAIT 0.452 = 0.026 0.368 0.011
1.294
0.009
0.205 0.011
LHS 0.439 = 0.008
0.316 0.011
0.270
0.222 + 0.012
50% Quantile
Random 0.021 0.00000000111
0.008 + 0.001
0.005 + 0.001
0.003 0.001
SBAL 0.021 = 0.005 0.016
0
0.0001
LCMD 0.021 + 0.005 0.020
0.016
0.006 0.009
Core-Set0.0211 = 0.005 0.022
0.009 0.012
Top-K 0.021 + 0.005
0.018 0.002
0.012 +
0.010
BAIT 0.021 + 0.005 0.020 0.003
0.008 0.001
0.005 0.000
LHS 0.019 + 0.001 0.011 + 0.002
0.007 + 0.001
0.005 + 0.001
0.003 + 0.001
95% Quantile
Random 0.106 +
0.231
0.0240.143 0.011
0.094 0.006
SBAL 0.603 0.16 0.066
0.255 0.031
0.163
0.119
LCMD 0.603 0.16
0.458
0.024
0.035
0.140 0.022
Core-Set0.600 0.166 0.501
=
0.425
0.225
0.213 0.053
Top-K 0.603 = 0.106 0.011
0.340
0.026
0.237
0.188 0.016
BAIT 0.603 0.106 0.450
0.051
0.269 = 0.043
0.163
0.100 + 0.012
LHS 0.572 + 0.0200.352 + 0.065
0.238 + 0.027
0.148 + 0.016
0.091 + 0.006
99% Quantile
Random
2.368 + 0.153 1.844
1.382 + 0.117 1.040 + 0.092
0.708 + 0.048
SBAL 2.368 + 0.110 1.655
0.137 + 0.1000.844.
0.103
0.619 + 0.093
LCMD 2.368 0.153
1811
1.400
1.151 0.123
0.802 0.149
Core-Set + 0.153
1.920
1.571 0.090
1.230 0.046
0.982 + 0.202
Top-K 2.368 = 0.153
1.126
1.356
0.086
0.873 0.119
BAIT 2.368
= 0.153
1.782
1.265 0.131
0.863 + 0.051
0.607 + 0.055
LHS 2.296 0.053
1,844 0.160
1.426 0.089
1.036
0.082
0.667 + 0.058
Table 5: Error metrics on KS.
28

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Published as a conference paper at ICLR 2025
Iteration
2
3
RMSE x10
Random
4.651 + 1.293
1.114:1.121
2.609 + 0.466
1.630 0.257
1.108 0.117
SBAL
4.651 + 1.293
1.597
0.931 = 0.125
0.496 0.087
0.318 0.048
LCMD
4.651 + 1.293
0.957 = 0.114
0.609
0.004
Core-Set
4.651 1.293
1.596 0.235
1.033 0.076
0.761 0.230
0.424
Top-K
4.651 + 1.293
1.678 0.099
0.904 0.101
0.529 0.103
0.373
0.077
BAIT
4.651 + 1.293
1.415 + 0.187
0.900
0.102
0.660 0.159
0.424 0.124
LHS
5.130 + 0.808
3.626 + 1.011
2.668 + 0.383
1.852 + 0.301
1.312 + 0.144
50% Quantile x10-2
Random
0.238 + 0.025
0,166 + 0,036
0.125 + 0.021
0.005
0.065 + 0.004
SBAL
0.238 + 0.025
0.200 0.024
0.125 + 0.009
0.076 + 0.008
0.052 + 0.004
LCMD
0.238 + 0.025
0.171
0.128 + 0.015
0.083 0.008
0.004
Core-Set
0.238 + 0.025
0.224:0.077
0.168 0.020
0.143
0.083 0.009
Top-K
0.238 + 0.025
0.211
0.155 0.016
0.111 0.015
0.073 + 0.008
BAIT
0.238 + 0.025
0.186
0.146 0.011
0.108
0.011
0.080 + 0.006
LHS
0.249 + 0.030
0.145 + 0.022
0.117 + 0.019
0.085 - 0.011
0.066 + 0.003
95% Quantile x10-2
Random
2.373
+.619+0.222 0.222
1.090 0.050
0.695 0.039
0.516 0.019
SBAL
2.373:1:.00
1.723
0.980 + 0.070
0.510 0.036
0.313 0.014 0.014
LCMD
2.373 0.220
1.485 + 0.121
1.038 + 0.087
0.609 0.061
0.000
Core-Set
2.373 0.220
1.389 0.126
1.102 0.469
0.598 0.095
Top-K
2.373 + 0.220
1.236 = 0.099
0.739 0.151
0.416 0.039
BAIT
2.373 + 0.220
= 1.567 0.152
1.121 = 0.085
0.753 - 0.075
0.515 0.047
LHS
2.537 + 0.213
1.516 + 0.098
1.080 + 0.098
0.709 + 0.057
0.530 + 0.013
99% Quantile -
Random 10.192 + 1.523
7.260 + 1.226
4.741 + 0.281
0.2277
1.70 0.099
SBAL 10.192 + 1.523
2.701 + 0.251
1.433 + 0.070
0.896 + 0.053
LCMD 10.192 1.523
4.198 + 0.103
2.787 +.2110
0.991 0.038
Core-Set 10.192 + 1.523
+ (056=0.89)
3.526 0.2112
2.638 1.069
1.446 1 0.290
Top-K 10.192 + 1.523
5.382 + 0.373
0.181
1.756 0.448
0.972 + 0.092
BAIT 10.192 1.523
4.290 + 0.307
2.896 - 0.141
1.939
+ 0.172
1.301 + 0.104
LHS 10.785 + 1.740
6.863 + 0.578
4.778 + 0.272
3.090 0.546
1.874 + 0.056
Table Error metrics on CE.
29

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Published as a conference paper at ICLR 2025
Iteration
2
RMSE
Random
3.054 + 1.276
1.966 : 0.248
1.293 + 0.092
0.997 + 0.079
0.695
SBAL
3.054 1.276
+
1.347 +
0.097
0.007
LCMD
2.291 1.276
0.856 0.092
Core-Set
3.054 1.276
2
1
1.232 0.160
0.753
Top-K
2.586 1.276
1.467 =
0.986 = 0.223
0.618 0.087
BAIT
3.054 1.276
3.728 + 1.544
1.649 = 0.262
1.181 0.262
0.599 = 0.072
LHS
2.635 + 0.326
2.159 + 0.480
1.269 + 0.099
0.987 0.130
0.669 + 0.055
50% Quantile
Random
1.158 + 0.387
0.758 0.122
0.479 + 0.044
0.347 + 0.043
0,206 0.019
SBAL
1.158 + 0.387
0.893
0.589 + 0.084
0.371 0.055
0.231
+ 0.046
LCMD
1.158 + 0.387
1.028=0049)
0.604 + 0.065
0.35140004)
0.196
- 0.017
Core-Set
1.158 + 0.387
1.108 0.282
0.963 = 0.301
0.102
0.313 0.093
Top-K
1.158 + 0.387
1.186 0.391 0.391
0.702 + 0.069
0.462 0.101
0.272 + 0.047 0.047
BAIT
1.158 + 0.387
1.216 0.293
0.758 0.105
0.496 0.060
0.256
+ 0.047
LHS
1.078 + 0.086
0.832 + 0.207
0.521 + 0.068
0.348 + 0.080
0.209 + 0.026
95% Quantile
Random
6.356 3.150
3.944 0.632
2.580 + 0.256
1.942 0.145
1.247 0.151
SBAL
6.356 3.150
4.236 0.939
2.709 + 0.405
1.696 + 0.250
1.149 0.188
LCMD
6.356 3.155
2.264
2.719 0.314
1.688 0.209
1.055 + 0.102
Core-Set
6.356 3.150
2.009
3.998 + 1.348
2.540 + 0.326
1.519 + 0.523
Top-K
6.356 3.150
5.444
3.022 - 0.373
1.988 0.530
1.225 + 0.189
BAIT
6.356 3.150
7.403
3.439 + 0.543
2.428 + 0.581
1.170 + 0.156
LHS
5.313 + 0.491
4
= 1.146
2.477 = 0.291
1.926 + 0.301
1.267 + 0.151
99% Quantile
Random 11.293 + 4.832
7.296 + 0.897
4.860 + 0.482
3.687 + 0.291
2.537 + 0.215
SBAL 11.293 + 4.832
7.290 + 1.582
4.720 = 0.761
2.969 0.315
2.035 0.267
LCMD 11.293 + 4.832
4.6337
4.651 0.522
0.388
2.095
Core-Set 11.293 + 4.832
6.103
+
2.649 + 0.796
Top-K 11.293 + 4.832
8.677
4.835 = 0.516
3.305 0.719
2.098 + 0.334
BAIT 11.293 + 4.832
14.736 + 6.750
5.570 + 1.124
4.182 + 1.347
2.126 + 0.194
LHS
9.637 + 1.813
8.103 + 1.884
4.582 + 0.325
3.596 0.326
2.500 + 0.223
Table 7: Error metrics ID CNS.
30

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Published as a conference paper at ICLR 2025
Iteration
2
RMSE
Random
2.662 + 0.339
2.162 + 0.029
1.856 + 0.106
1.572 + 0.072
1.362 + 0.065
SBAL
2.662 0.339
1.979
0.226
1.790 + 0.203
1.458 0.140
1.205 - 0.027
LCMD
2.662 + 0.339
0.299
1.734 0.189
1.356 + 0.081
1.277 0.083
Core-Set
2.662 + 0.339
2.322 0.350
1.731 + 0.168
1.613
1.343 0.186
Top-K
2.662 + 0.339
1.122
2.070 = 0.368
1.623 = 0.524
1.313 0.106
BAIT
2.662 + 0.339
2.167 = 0.164
1.715 - 0.269
1.426 + 0.209
LHS
2.459 + 0.081
2.134 + 0.148
1.829 + 0.098
1.514 + 0.059
1.344 + 0.038
50% Quantile
Random
0.447 0.119 0.156
0.356 + 0,111
0.266
0.209
0.034
SBAL
0.506
0.116
0.543 + 0.344
0.336 0.063
0.295 0.053
LCMD
0.119
0.374
0.412 0.234
0.067
0.082
Core-Set
0.506 0.119
0.562
0.433
0.408 0.120
Top-K
0.506 0.119
0.653
0.521 0.133
0
0.400 + 0.065
BAIT
0.506
0.119
0.336
0.392 = 0.076
0.335
0.069
0.311 + 0.093
LHS
0.553 + 0.132
0.503 + 0.068
0.304 + 0.035
0.264 + 0.066
0.233 + 0.041
95% Quantile
Random
4.421 + 0.630
0.151
2.828 0.314
2.317
1.927 - 0.170
SBAL
4.421 + 0.630
4.30
4.33
2.310 0.349
1.821 + 0.128
LCMD
4.421 0.630
3.263 0.561
0.351 2.758
2.025 0.177
2.003 0.326
Core-Set
4.421 + 0.630
4.235
2.690 + 0.396
2.189 + 0.437
Top-K
4.421 + 0.630
5.009
3.891 + 0.921
2.911 + 1.392
2.238 + 0.289
BAIT
4.421 + 0.630
4.700
2.783 + 0.547
2.238 + 0.404
1.900 0.273
LHS
4.173 = 0.299
3.283 = 0.240
2.840 = 0.230
2.250 0.102
1.926 + 0.087
99% Quantile
Random 11.378 + 1.863
9.235+0.255
7.754 + 0.507
6.620 0.340
5.735 + 0.320
SBAL 11.378 + 1.863
1.062
7.195 + 0.786
0.573
4.933 + 0.112
LCMD 11.378 + 1.863
0.926 8.196
7.229 = 0.609
5.569 + 0.362
5.265 0.399
Core-Set
11.378 + 1.863
9.739
7.263 = 0.707
6.646 0.794
5.404 + 0.722
Top-K 11.378 + 1.863
+
8.531 + 1.478
6.466 + 2.101
5.237 + 0.417
BAIT
11.378 + 1.863
8.948 + 0.487
7.140 + 1.168
5.923 + 0.922
5.059 + 0.598
LHS
10.422 + 0.367
8.800 + 0.769
7.727 0.531
6.374 0.198
5.611 + 0.132
Table 8: Error metrics 2D CNS.
31

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Published as a conference paper at ICLR 2025
Iteration
3
4
RMSE
Random
3.159 0.915
2.550 + 0.338
2.022 0.201
SBAL
3.159 + 0.915
2.265
1.901 0.009
1.680 + 0.098
LCMD
+.959
4.382
2.011 +
1.795 = 0.032 0.032
Core-Set
3.159 0.915
1.383:0.00
1.890 + 0.199
1.632 + 0.197
Top-K
3.159 0.915
2.635 0.216
2.056 0.194
1.823 + 0.231
BAIT
3.159 = 0.915
2.205 + 0.125
1.959 + 0.098
1.697 = 0.133
LHS
3.180 + 1.095
2.567 0.648
2.297 + 0.124
2.017 = 0.226
50% Quantile
Random
1.092 + 0.187
0.599 + 0.070
0.381 + 0.083
0.268 + 0.046
SBAL
1.092 + 0.187
0.639 0.072
0.420 0.048
0.332 0.054
LCMD
1.092 + 0.187
0.862 + 0.435
0.393
Core-Set
1.092 = 0.187
0.852 0.180
0.455
0.044
Top-K
1.092 + 0.187
0.952 + 0.404
0.615 0.138
0.598 0.285
BAIT
1.092 + 0.187
0.731
0.424
0.510 + 0.408
0.303 + 0.033
LHS
1.090 + 0.215
0.585 + 0.159
0.448 + 0.163
0.272 + 0.050
95% Quantile
Random
5.519 + 0.857
4.255
0.480
3.885 + 0.682
3.162 0.150
SBAL
5.519 0.857
4.058 0.873
3.215 + 0.143
2.725 0.442
LCMD
5.519 + 0.857
4.289
1.666
0.337
3.020
Core-Set
5.519 0.857
4.520 1.904
3.549
+.8041
Top-K
5.519 + 0.857
4.935 + 1.120
3.878 + 0.720
3 0.424
BAIT
5.519 + 0.857
3.945 + 0.257
3.457 + 0.425
2.831 = 0.416
LHS
5.626 + 1.988
4.108 + 0.743
3.872 + 0.178
3.157 + 0.449
99% Quantile
Random 12.929 + 6.254
11.013 + 1.485
10.187 + 2.298
4.527+0.916
SBAL 12.929 + 6.254
9.128 2.003
7.844 + 0.410
0.200
LCMD 12.929 6.254
9.245 2.225
8.367 + 0.805
7.491
Core-Set
12.929 + 6.254
9.062 + 2.095
7.649 + 0.618
6.713 + 0.686
Top-K 12.929 + 6.254
9.744 + 2.160
8.253 + 0.703
7.072 0.733
BAIT 12.929 + 6.254
8.710 + 0.265
8.022 + 0.419
7.073 + 0.532
LHS
13.314 6.100
11.343 4.375
9.730 + 0.983
8.411 = 1.143
Table 9: Error metrics 3D CNS.
32

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Published as a conference paper at ICLR 2025
Iteration
2
3
4
Pearson
KS
87.1
3.8
84.9
2.3
78.0
5.4
80.5
3.7
CE
49.2 16.2
62.0
14.6
41.3
22.1
20.9
1D CNS
49.7 14.8
67.0
592
6.1
55.6
5.1
2D CNS
78.2 - 6.4
18.9-18.00
90.8
2.7
94.3
2.0
3D CNS
41.4 +: 3.7
11.9
74.9 + 2.6
Burgers M = 2 92.0 + 6.3 71.3 + 27.1 71.4 + 11.5
67.9 + 18.4
Burgers M = 89.5 + 8.2 60.9 26.7
67.9 + 19.6
Spearman
KS
86.4 + 2.8 83.0 + 2.8 83.9 + 4.2 82.7
+ 0.4
CE
87.4
1.7
83.9 2.1
81.2
80.5
1.5
ID CNS
71.5
9.2
54.4
14.3
66.4
5.1
68.1
3.2
2D CNS
94.6
2.4
93.4
2.3
91.1
3.9
93.4
1.6
3D CNS
72.7
3.1
86.0
0.6
91.6
0.8
Burgers M 87.5 -
2.7
83.2
11.0
75.2
5.0
73.7
5.3
Burgers M = 6 90.3 + 0.9
84.5
2.3 80.8 = 2.2
Table 10: Correlation coefficients in percent between the error and the uncertainty averages per trajectory, including the standard deviation. Computed for SBAL the main experiments well as ensemble size ablation experiment.
33

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Published as a conference paper at ICLR 2025
G OVERVIEW OF THE GENERATED DATASETS
In the following sections, we show visual examples of the data selected by random sampling and SBAL, and the marginal distributions of all PDE and IC parameters afterwards.
G.1 EXAMPLE TRAJECTORIES
Random
SBAL
0,0392
0.0055
0.0027
0,0066
0.5
0.5
0.0598
0.0015
0.0022
0.0010
01.4978
0.0019
0.0039
0.2346
0.5
0.5
0.2104
0,4735
0.0174
0.0012
0.0049
0.0014
0.0012
0.0010
0
2.0
2.0
Figure 11: Example ground truth trajectories of random and SBAL on Burgers. The number on the top left of the trajectories shows the PDE parameter
34

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Published as a conference paper at ICLR 2025
Random
SBAL
0,7216, 67.4120
0.525M
1.104, 2010
0.5
5,56-2, 98. 3340
2.5678, 24.9222
1,112, 26.2847
3,5690, 39.3524
3.8841, 2367
1.8506, 59.4168
1.1081. 64.0001
0.7657, 123159
0.5
0.6414,
2,4612,
0.5
L8126, 9.3366
3,5615, 920
0.12N2 13.1809
2.18T 36.3004
1,5107, 12.0133
3.7307, 41.3270
1,433, 15-461
0,8640, 65.91 80
L.51L 50.9152
L,070.. IL.8177
0.5
0
1.0
0
1.0
Figure 12: Example ground truth trajectories of random and SBAL on KS. The number the top left of the trajectories shows the parameters (v, L). The x-axis is shown in normalized values between and independent of the variable domain length L.
35

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Published as a conference paper at ICLR 2025
Random
SBAL
2.9463, 0,4000, 0.0807
2,0556, 184242, 0.9734
2.1247, 0.0088, 0.334
2.5724. 0.01344,
0.5
1.4311, 0.2430, 0.2484
2,9500, 0.3783, 0.8613
2,8920, 0,35,,, 0.9661
0.3584, 0.0729,0.9697
0.9697
1,9555, 3,313,, 0.9995
1,4883, 0,3816, 0,045S
0.7970, 0.0O 0.6774
2,8001, 0.047,, 0.0143
2.6695,
0.5
0.5
N.7727, 0.304, 0.705
L,088, 0.2728, 0.337
2.7249, 0.325,, 0.205
2.6186. 0,1440, 0.713
2.1078 00415, 0.9877
2.1199 0. 0.1722.0.177
1.437L 0,00H, 0.8870
2,3216, COMOS, 000477
20.0
40
20.0
40
20.0
40
20.0
Figure 13: Example ground truth trajectories random and SBAL on CE. The numbers on the top left of the trajectories show the PDE parameters (a, 8, 7).
36

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Published as a conference paper at ICLR 2025
Random
SBAL
0.0110
B.0022
0.5
0.0144
0:0717
0.0001
0.0001
0.5
0.0002
0,00CS
0.0007
0.00997
0.0003
0.D0097
0.0002
0.5
0.0600
0.0043
0.0414
0.0011
0.5
B.0267
0.0052
ILON10T
0.0024
0.0098
0.0OLO
0.0048
0.0145
0.5
0
Figure 14: Example ground truth trajectories of random and SBAL on 1D CNS. The number on the top left of the trajectories shows the PDE parameters (n = ().
37

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Published as a conference paper at ICLR 2025
t= 0.0
t= 0.2
t= 0.4
t 0.6
t= 0.8
t= 1.0
Vx
Vy
Figure 15: Example ground truth trajectory of 2D CNS.
38

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Published as a conference paper at ICLR 2025
& timestes:
0.001649, timestep:
0a
0
04
0.
0.
Ro
Ro
g
00
density
density
200
221
2.34
2.48
2.58
2.71
2.83
2.08
3.08
3.20
1.72
2,00
2.28
2.56
2.84
3.40
3,66
4.25
0.007983, 0.001649,
0.001649, timestep
1.
0.8
0g
20
Po
80
density
density
1.85
2.10
2.36
2.81
2.86
111
3.36
3.61
3,60
4,12
1.96
2.19
2.43
2.67
2.91
3.14
3.58
3.62
3.85
1.09
0.007983, 0.001649,
0.007983, 0.001649,
to
0.
0g
00
8
PO
density
density
1.96
2.19
2.43
2.67
2.90
3.14
3.38
3.62
3.05
4.09
1.94
2,20
45
2.71
2,96
3.47
3.72
3.88
4.23
Figure 16: XY, YZ, and XZ. planar views of the density channel from a random 3D CNS trajectory in
the validation dataset representing a simulation of the compressible flow on the unit cube.
39

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Published as a conference paper at ICLR 2025
& timestes:
0.001649, timestep:
2.
0g
z
04
0.
0.
Ro
Ro
g
velocity-x
velocity-x
4.23
0.98
-0.4
0.49
0.24
0.25
0.49
0.09
0,42
4.31
0.21
4.10
0.00
0.10
021
0.42
0.52
0,001649,
0.007983, 0.001649, timestep:
1.
0.8
0
g0
Po
80
velocity-x
velocity-x
0.26
0.19
4.13
0.00
0.08
0.3
0.1
026
0.23
0.11
0.14
0.00
4.05
0.00
0.05
0.09
014
0.18
0.007983, 0.001649,
0.007983, 0.001648,
to
og
Bo
P
8
velocity-x
velocity-x
8.17
0.14
9.10
4.07
9.03
4.00
0.03
007
0.10
B:14
8.14
4.11
4.08
4.06
4.03
0.00
0.03
0.06
0.9
0.1
Figure 17: XY, YZ, and XZ. planar views of the velocity (क) channel from a random 3D CNS
trajectory in the validation dataset showing simulation of the compressible flow on the unit cube.
40

---

Published as a conference paper at ICLR 2025
& timestes:
0.001649, timestep:
08
z
04
04
0.
0.
Ro
Ro
velocity-y
velocity-y
122
0.97
4.73
0.48
0.24
0.00
0.49
0.73
0.97
0.46
0.38
0.29
4.19
0.00
0.10
0.19
0.29
0.38
0.007983, 0.001649,
0.007983, 0.001649, timestep:
3.
6.8
0g
20
Po
80
velocity.y
velocity-y
0.20
0.15
0.10
0.05
0.00
0.05
040
0.5
0.20
026
4.16
0.12
0.00
0.04
0.00
0.04
0.8
016
0.1
0.007983, 0.001649,
0.007983, 0.001649,
to
og
Bo
K
P
g
P
00
velocity-y
velocity-y
a.1
200
20.05
4.03
0.00
003
0.5
008
a.l
0.13
0.08
4.06
4.04
4.02
4.00
0.2
0.04
0.06
0.0
0.10
Figure 18: XY, YZ, and XZ. planar views of the velocity ( channel from a random 3D CNS
trajectory in the validation dataset showing simulation of the compressible flow on the unit cube.
41

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Published as a conference paper at ICLR 2025
& timestes:
0.001649, timestep:
2.
0a
z
04
8
0.
0.
Ro
Ro
g
velocity-z
velocity-2
0.97
0.75
0.49
0.24
0.00
0.49
0.98
1.22
0.50
4.40
0.30
0.20
0.00
0.10
0.20
0.30
0.40
0,001649,
0.007983, 0.001649, timestep:
1.
1.
0.8
0e
06
20
Po
80
velocity-z
velecity-2
0.39
0.2
0.20
0.13
0.07
0.00
0.1
02
0.17
0.2
0.08
4.04
0.00
0.44
0.08
0.2
0.17
0.1
0.007983, 0.001649,
0.007983, 0.001649,
to
og
Bo
P
velocity-z
velocity-2
0.11
0.09
0.06
0.03
4.00
603
0.08
0.88
021
0.14
4.11
4,00
0.07
2.04
0.02
0.0
0.22
6o5
0.07
0.8
Figure 19: XY, YZ, and XZ. planar views of the velocity (ž) channel from a random 3D CNS
trajectory in the validation dataset showing simulation of the compressible flow on the unit cube.
42

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Published as a conference paper at ICLR 2025
& timestes:
0.001649, timestep:
2.
0a
04
0.
0.
Ro
Ro
g
90
pressure
pressure
3.89
5.41
6.92
8.44
9.96
13.00
14.52
04
17.56
8.23
8.99
8.78
10.52
11.28
12.05
12.61
13.58
14.34
15.10
0,001649,
0.007983, 0.001649, timestep:
1.
1.
0.8
0g
06
20
Po
80
presture
pressure
9.07
9.60
14
10.08
11.23
11.75
12.29
12.03
37.37
$3,00
8.78
10.12
10.48
10.84
11.20
11.50
21.02
12.28
12.64
13.00
0,007983, 0.001649, timestep:
0.007983, 0.001648, timestep:
to
og
k
P
P
gressure
pressure
10.09
10.37
10.66
10.95
11.24
11.53
11.1
12.10
12.39
12.68
10.41
10.66
10.91
11.16
11.1
11.66
1192
12.17
12.22
12.67
Figure 20: XY, YZ, and XZ planar views of the pressure channel from a random 3D CNS trajectory
in the validation dataset representing simulation of the compressible flow on the unit cube.
43

---

Published as a conference paper at ICLR 2025
G.2 IC PARAMETER MARGINAL DISTRIBUTIONS
Figures 21-26 show the marginal distributions of the random parameters of the IC generators, i.e., the random variables drawn which are then transformed using deterministic function the actual IC. For example, the KS IC generator draws amplitudes and phases from a uniform distribution and uses them afterward for the superposition of sine waves. multiple numbers drawn from each type of variable, we them together, c.g., in the case of KS, multiple amplitudes are drawn for the different waves, but Figure 22 only shows the distribution all amplitude variables mixed. The distribution curves for continuous variables are computed using kernel density estimation. The shaded areas (vertical lines for discrete variables) show the standard deviation between the marginal distributions of different random seeds.
Burgers
2
Rel. Donsity
0.0
0.5
1.0
0.0
3,2
6.3
0
Falsc
Truc
Amplitude
Number
Apply Abs.
Rel. Density
False
True
-1
0.1
0.3
0.5
0.5
0.7
0.9
Apply Window
Sign
Xi
Xn
Random
SBAL
L.CDD
Core-Set
Top-K
BAIT
L.HS
Figure 21: Marginal distribution of the parameters of the ICs sampled by the AL methods for Burgers. Displayed the ratio the density of the uniform distribution.
KS
Density
1.1
0.0
0.5
1.0
0.0
3.2
6.3
Amplitude
Phase
Wavve Number
Random
SBAL
LCMD
Core-Sel
Top-K
BAIT
LIIS
Figure 22: Marginal distribution of the parameters of the ICs sampled by the AL methods for KS. Displayed as the ratio to the density of the uniform distribution.
44

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Published as a conference paper at ICLR 2025
CE
Rel. Density
1.0
0.0
0.4
0.0
3.2
6.3
Amplitude
Phase
Wave Number
Random
SBAL
LCMD
Core-Set
Top-K
BAIT
LHS
Figure 23: Marginal distribution of the parameters of the ICs sampled by the AL methods for CE. Displayed as the ratio to the density of the uniform distribution.
ID CNS
1.5
Rel. Donsity
1.0
0.5
0.0
0.5
0.0
3.2
6.3
Amplitude
Phase
Wave Number
Apply Abs.
1.5
Rel. Density
1.0
False
True
False
True
0.1
0.3
0.5
0.6
0.7
0.9
Apply Window
Sign
Random
SBAL
LCMD
Core-Set
Top-K
BAIT
LHS
Figure 24: Marginal distribution of the parameters of the ICs sampled by the AL methods for 1D CNS. Displayed the ratio the density of the uniform distribution.
45

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Published as a conference paper at ICLR 2025
2D CNS
Rel. Donsity
2.
0.0
3.2
0.6
1.0
0.1
5.1
10.0
0.1
10.0
Phuse
Mach
Pn
To
Rel. Density
0.0
0.1
0.4
0.8
0.1
0.5
0.6
0.7
Dp
4p
Rel. Density
0.1
0.3
0.5
0.6
0.7
0.9
False
Truc
Y*
Apply
Random
SBAL
LCMD
Core-Set
Top-K
BAIT
LII
Figure 25: Marginal distribution the parameters of the ICs sampled by the AL methods for 2D CNS. Displayed the ratio the density of the uniform distribution.
46

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Published as a conference paper at ICLR 2025
3D CNS
Rel. Density
0.0
3.2
6.3
0.1
0.6
1.0
0.1
5.1
10.0
0.1
5.1
10.0
Phase
Mach
Rel. Deasity
0.0
0.1
0.3
0.0
0.4
0.8
0.1
0.3
0.5
0.6
0.7
0.9
Ap
Ap
XL
Rel. Density
0.1
0.3
0.5
0.6
0.7
0.9
0.1
0.3
0.5
0.6
0.7
0.9
YR
2L
Rel. Density
False
True
Apply Window
Random
SBAL
LCMD
Core-Sct
Top-K
BAIT
LHS
Figure 26: Marginal distribution of the parameters of the ICs sampled by the AL methods for 3D CNS. Displayed as the ratio to the density of the uniform distribution.
47

---

Published as a conference paper at ICLR 2025
G.3 PDE PARAMETER MARGINAL DISTRIBUTIONS
Similarly, Figures 27 and 28 show the KDE estimates of the dataset after the final AL iteration for the PDE parameters.
Burgers
KS
CE
2.5
Deasity
2.0
10
1.5
0.5
0
10
2.0
Density
1.5
1.0
0.5
50
100
0.0
0.2
0.4
B
2.0
1.5
1.0
0.5
0.5
1.0
Random
SBAL
LCMD
Core-Set
Top-K
BAIT
LHS
Figure 27: Marginal distribution of the PDE parameters of Burgers, KS, and CE, including the standard deviation between different runs. Displayed as the ratio to the density of the test distribution.
48

---

Published as a conference paper at ICLR 2025
ID CNS
2D CNS
3D CNS
2.0
1.5
1.5
1.0
1.0
1.0
0.5
0.5
0.5
10
10
10
10
10
10"
10
2.0
Density
1.5
1.0
1.0
0.5
0.5
10
10"
10
10"
10
Random
SBAT.
L.CDD
Core-Set
Top-K
BAIT
L.HS
Figure 28: Marginal distributions the ID, 2D, and 3D CNS PDE parameters, including the standard deviation between different runs. Displayed as the ratio to the density of the test distribution.
49


Reviews:
[{"review_id": "3ofFFxd1tn", "reviewer": "Reviewer_zzgS", "summary": "This paper provided a bechmark called AL4PDE, which unifies active learning (AL) with neural PDE solvers. Specifically, it studies how several state-of-the-art neural surrogate model may be applied to solve parametric PDEs under a solver-in-the-loop (AL) setting. A complete set of numerical experiments on various tasks is included to justify the effectiveness of AL based methods compared to methods based on random sampling.", "questions": "The parameter $c$ mentioned in line 96-97 (referred to as the field variables or channels) seems a bit ambiguous here as the following PDE doesn't contain anything about $c$. Would it be possible for the authors to provide more detailed explanation for the field variable/channel $c$ here? (This also highly relates to the parameter $N_c$ appearing in equations (3) and (5).)\n\nReferences:\n\n[1] Bruna, Joan, Benjamin Peherstorfer, and Eric Vanden-Eijnden. \"Neural Galerkin schemes with active learning for high-dimensional evolution equations.\" Journal of Computational Physics 496 (2024): 112588.\n\n[2] Gajjar, Aarshvi, Chinmay Hegde, and Christopher P. Musco. \"Provable active learning of neural networks for parametric PDEs.\" In The Symbiosis of Deep Learning and Differential Equations II. 2022.\n\n[3] Gao, Wenhan, and Chunmei Wang. \"Active learning based sampling for high-dimensional nonlinear partial differential equations.\" Journal of Computational Physics 475 (2023): 111848.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Extensive numerical experiments on multiple PDEs are provided to validate the effectiveness of the proposed methodology. Also, details about the numerical experiments, such as the neural network models and training procedures, are included for the sake of completeness.", "weaknesses": "1. Though the authors have conducted a literature review on how AL has been used for solving other problems from scientific ML, such as PINN and direct prediction, it seems to the reviewer that the authors have missed a few important references like [1,2]. It might be meaningful for the authors to include these work and briefly discuss them in the introduction. \n\n2. Given that this work aims for a complete benchmark on various tasks, the authors might consider including some more experiments on high-dimensional PDEs, just like the setting of [3].", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5pBXoqSX1a", "reviewer": "Reviewer_4k62", "summary": "This paper proposes a benchmark framework for neural PDE solvers under active learning (AL) settings (AL4PDE). It provides a modular benchmark with various parametric PDEs and AL methods. The experimental results show that AL significantly reduces average and worst case errors compared to random sampling and yields reusable datasets across experiments.", "questions": "Please refer to the strengths and weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper is well-presented and easy to follow.\n\nThe proposed framework is novel in extending neural PDE methods with active learning methods.\n\nThe benchmark includes various batch selection strategies and neural PDE solvers, covering recent and classical works.", "weaknesses": "One key benefit of AL is data efficiency, which is also stressed in the paper. It is important to show how much data reduction can be achieved with reasonable model performance.\nCurrent experiment section only shows performance comparison of different active learning methods and lacks the \"offline\" performance, which is training the model with full dataset and evaluate its performance. \n\nAt line $88$, the author claims \"We demonstrate that using AL can result in more accurate surrogate models trained in less time.\" As mentioned above, this claim is not supported with empirical evidence as the experimental section only compares active learning performance, which cannot demonstrate improvement in accuracies regarding offline performance.\n\nThe novelty of this framework seems limited, as it is a combination of existing AL and neural PDE methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "D1QB665hV3", "reviewer": "Reviewer_U4Je", "summary": "Thie article introduces an active learning benchmark for neural PDE solver. It compare exploration-exploitation tradeoffs based uncertainty (epsitemic uncertainty of an ensemble of models with top-K and SBAL) or features (using dimensionality reduction using Gaussian sketching with Core-Set and LCMD). The authors then show a benchmark of these method on 1D and 2D parametric PDEs adding the baseline of sampling uniformly at random to represent the lack of active learning.", "questions": "Could you please add baselines form DoE?\n\nCould you link your benchmark code to existing open source code for uncertainty quantification (e.g. UQ 360)?\n\nCould you explain the tradeoffs of reducing the dimensionality of features vs. implementing translational invariance in terms of data efficiency of the feature-based AL?\n\nCould you add examples that do not use periodic boundary conditions?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Contributing a benchmark in active learning for PDE solvers fills a needed gap in computational infrastructure for PDE solvers that is key to the central challenge of data efficiency.\nThe article is pedagogical and presents clearly the capability of the benchmark.", "weaknesses": "The authors should help compare methods of Bayesian active learning and those of the field of design of experiments (DoE), which is missing in the literature review. For example, instead of using a baseline of uniform sample, Latin Hypercube sampling should be provided, as well as more sophisticated DoE methods. This benchmark effort is an opportunity to bridge these areas of research and communities that try to solve the same problem with a slightly different point of view and a different approach.\n\nThe benchmark should broaden the UQ methods by connecting to existing efforts (for example, the open-source UQ 360). Any UQ method that provide a confidence interval should suffice for active learning as the spread of the confidence interval can be a proxy of the uncertainty.\n\nIn the implementation details, the tradeoffs of the choice of taking the spatial average over the features to make make feature-based AL translation invariant are not discussed. It seems that the averaging creates a significant dimensionality reduction that may outweigh the benefits of a translational invariance in terms of data efficiency.\n\nAll the implementations use periodic boundary conditions, which significantly limits the scope of applications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rXKns86Gq9", "reviewer": "Reviewer_KuN9", "summary": "The paper proposes a modular active learning (AL) framework for training surrogate models of partial differential equation (PDE) solvers. It introduces a numerical solver for generating PDE samples, several surrogate models, batch selection strategies, and acquisition functions designed for active learning within this framework.", "questions": "No question", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The well-designed modular framework provides a solid foundation for further research on active learning in the context of PDEs.", "weaknesses": "1. Although the framework is tailored for PDE problems, the implemented acquisition functions are orthogonal to PDE problems i.e. they are AL methods that are used in general domains. As a framework of AL for PDE, at least some PDE-specific AL methods such as adaptive sampling [1], also mentioned in Related Work, should be also implemented.\n1. The paper’s scope in terms of the surrogate models, acquisition functions, and types of PDEs studied is quite limited, impacting its practical applicability.\n\n[1] W. Gao and C. Wang, Active Learning Based Sampling For High-dimensional Nonlinear Partial Differential Equations, Journal of Computational Physics, Vol. 475, 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)