pwc-archive/papers-with-abstracts huggingface dataset:
Adiabatic Encoding of Pre-trained MPS Classifiers into Quantum Circuits
Authors: Keisuke Murota
Date: 2025-04-12 00:00:00

Although Quantum Neural Networks (QNNs) offer powerful methods for classification tasks, the training of QNNs faces two major training obstacles: barren plateaus and local minima. A promising solution is to first train a tensor-network (TN) model classically and then embed it into a QNN.\ However, embedding TN-classifiers into quantum circuits generally requires postselection whose success probability may decay exponentially with the system size. We propose an \emph{adiabatic encoding} framework that encodes pre-trained MPS-classifiers into quantum MPS (qMPS) circuits with postselection, and gradually removes the postselection while retaining performance. We prove that training qMPS-classifiers from scratch on a certain artificial dataset is exponentially hard due to barren plateaus, but our adiabatic encoding circumvents this issue. Additional numerical experiments on binary MNIST also confirm its robustness.
Covariant Gradient Descent
Authors: Dmitry Guskov, Vitaly Vanchurin
Date: 2025-04-07 00:00:00

We present a manifestly covariant formulation of the gradient descent method, ensuring consistency across arbitrary coordinate systems and general curved trainable spaces. The optimization dynamics is defined using a covariant force vector and a covariant metric tensor, both computed from the first and second statistical moments of the gradients. These moments are estimated through time-averaging with an exponential weight function, which preserves linear computational complexity. We show that commonly used optimization methods such as RMSProp, Adam and AdaBelief correspond to special limits of the covariant gradient descent (CGD) and demonstrate how these methods can be further generalized and improved.
Complex multiannual cycles of Mycoplasma pneumoniae: persistence and the role of stochasticity
Authors: Bjarke Frost Nielsen, Sang Woo Park, Emily Howerton, Olivia Frost Lorentzen, Mogens H. Jensen, Bryan T. Grenfell
Date: 2025-04-15 00:00:00
Tasks: Sensitivity

The epidemiological dynamics of Mycoplasma pneumoniae are characterized by complex and poorly understood multiannual cycles, posing challenges for forecasting. Using Bayesian methods to fit a seasonally forced transmission model to long-term surveillance data from Denmark (1958-1995, 2010-2025), we investigate the mechanisms driving recurrent outbreaks of M. pneumoniae. The period of the multiannual cycles (predominantly approx. 5 years in Denmark) are explained as a consequence of the interaction of two time-scales in the system, one intrinsic and one extrinsic (seasonal). While it provides an excellent fit to shorter time series (a few decades), we find that the deterministic model eventually settles into an annual cycle, failing to reproduce the observed 4-5-year periodicity long-term. Upon further analysis, the system is found to exhibit transient chaos and thus high sensitivity to stochasticity. We show that environmental (but not purely demographic) stochasticity can sustain the multi-year cycles via stochastic resonance. The disruptive effects of COVID-19 non-pharmaceutical interventions (NPIs) on M. pneumoniae circulation constitute a natural experiment on the effects of large perturbations. Consequently, the effects of NPIs are included in the model and medium-term predictions are explored. Our findings highlight the intrinsic sensitivity of M. pneumoniae dynamics to perturbations and interventions, underscoring the limitations of deterministic epidemic models for long-term prediction. More generally, our results emphasize the potential role of stochasticity as a driver of complex cycles across endemic and recurring pathogens.
Visual anemometry of natural vegetation from their leaf motion
Authors: Roni H. Goldshmid, John O. Dabiri, John E. Sader
Date: 2025-04-14 00:00:00

High-resolution, near-ground wind-speed data are critical for improving the accuracy of weather predictions and climate models,$^{1-3}$ supporting wildfire control efforts,$^{4-7}$ and ensuring the safe passage of airplanes during takeoff and landing maneouvers.$^{8,9}$ Quantitative wind speed anemometry generally employs on-site instrumentation for accurate single-position data or sophisticated remote techniques such as Doppler radar for quantitative field measurements. It is widely recognized that the wind-induced motion of vegetation depends in a complex manner on their structure and mechanical properties, obviating their use in quantitative anemometry.$^{10-14}$ We analyze measurements on a host of different vegetation showing that leaf motion can be decoupled from the leaf's branch and support structure, at low-to-moderate wind speed, $U_{wind}$. This wind speed range is characterized by a leaf Reynolds number, enabling the development of a remote, quantitative anemometry method based on the formula, $U_{wind}\approx740\sqrt{{\mu}U_{leaf}/{\rho}D}$, that relies only on the leaf size $D$, its measured fluctuating (RMS) speed $U_{leaf}$, the air viscosity $\mu$, and its mass density $\rho$. This formula is corroborated by a first-principles model and validated using a host of laboratory and field tests on diverse vegetation types, ranging from oak, olive, and magnolia trees through to camphor and bullgrass. The findings of this study open the door to a new paradigm in anemometry, using natural vegetation to enable remote and rapid quantitative field measurements at global locations with minimal cost.
Physical Scales Matter: The Role of Receptive Fields and Advection in Satellite-Based Thunderstorm Nowcasting with Convolutional Neural Networks
Authors: Christoph Metzl, Kianusch Vahid Yousefnia, Richard Müller, Virginia Poli, Miria Celano, Tobias Bölle
Date: 2025-04-14 00:00:00

The focus of nowcasting development is transitioning from physically motivated advection methods to purely data-driven Machine Learning (ML) approaches. Nevertheless, recent work indicates that incorporating advection into the ML value chain has improved skill for radar-based precipitation nowcasts. However, the generality of this approach and the underlying causes remain unexplored. This study investigates the generality by probing the approach on satellite-based thunderstorm nowcasts for the first time. Resorting to a scale argument, we then put forth an explanation when and why skill improvements can be expected. In essence, advection guarantees that thunderstorm patterns relevant for nowcasting are contained in the receptive field at long lead times. To test our hypotheses, we train ResU-Nets solving segmentation tasks with lightning observations as ground truth. The input of the Baseline Neural Network (BNN) are short time series of multispectral satellite imagery and lightning observations, whereas the Advection-Informed Neural Network (AINN) additionally receives the Lagrangian persistence nowcast of all input channels at the desired lead time. Overall, we find only a minor skill improvement of the AINN over the BNN when considering fully averaged scores. However, assessing skill conditioned on lead time and wind speed, we demonstrate that our scale argument correctly predicts the onset of skill improvement of the AINN over the BNN after 2h lead time. We confirm that generally advection becomes gradually more important with longer lead times and higher wind speeds. Our work accentuates the importance of considering and incorporating the underlying physical scales when designing ML based forecasting models.
Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
Authors: Simon Adamov, Joel Oskarsson, Leif Denby, Tomas Landelius, Kasper Hintz, Simon Christiansen, Irene Schicker, Carlos Osuna, Fredrik Lindsten, Oliver Fuhrer, Sebastian Schemm
Date: 2025-04-12 00:00:00
Tasks: graph construction, Weather Forecasting

Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large value in high-resolution regional weather forecasts, focusing on accurate simulations of the atmosphere for a limited area. Initial attempts have been made to use machine learning for such limited area scenarios, but these experiments do not consider realistic forecasting settings and do not investigate the many design choices involved. We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method. This enables boundary conditions defined either from reanalysis or operational forecast data. Our approach employs specialized graph constructions with rectangular and triangular meshes, along with multi-step rollout training strategies to improve temporal consistency. We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration. Models are evaluated across both a Danish and a Swiss domain, two regions that exhibit different orographical characteristics. Verification is performed against both gridded analysis data and in-situ observations, including a case study for the storm Ciara in February 2020. Both models achieve skillful predictions across a wide range of variables, with our Swiss model outperforming the numerical weather prediction baseline for key surface variables. With their substantially lower computational cost, our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting.
DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation
Authors: Soyoung Yoo, Namwoo Kang
Date: 2025-04-15 00:00:00
Tasks: 3D geometry, Dataset Generation, Depth Estimation

Data-driven design is emerging as a powerful strategy to accelerate engineering innovation. However, its application to vehicle wheel design remains limited due to the lack of large-scale, high-quality datasets that include 3D geometry and physical performance metrics. To address this gap, this study proposes a synthetic design-performance dataset generation framework using generative AI. The proposed framework first generates 2D rendered images using Stable Diffusion, and then reconstructs the 3D geometry through 2.5D depth estimation. Structural simulations are subsequently performed to extract engineering performance data. To further expand the design and performance space, topology optimization is applied, enabling the generation of a more diverse set of wheel designs. The final dataset, named DeepWheel, consists of over 6,000 photo-realistic images and 900 structurally analyzed 3D models. This multi-modal dataset serves as a valuable resource for surrogate model training, data-driven inverse design, and design space exploration. The proposed methodology is also applicable to other complex design domains. The dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International(CC BY-NC 4.0) and is available on the https://www.smartdesignlab.org/datasets
Hemodynamic Markers: CFD-Based Prediction of Cerebral Aneurysm Rupture Risk
Authors: Reza Bozorgpour, Jacob R. Rammer
Date: 2025-04-11 00:00:00

This study investigates the influence of aneurysm evolution on hemodynamic characteristics within the sac region. Using computational fluid dynamics (CFD), blood flow through the parent vessel and aneurysm sac was analyzed to assess the impact on wall shear stress (WSS), time-averaged wall shear stress (TAWSS), and the oscillatory shear index (OSI), key indicators of rupture risk. Additionally, Relative Residence Time (RRT) and Endothelial Cell Activation Potential (ECAP) were examined to provide a broader understanding of the aneurysm's hemodynamic environment. Six distinct cerebral aneurysm (CA) models, all from individuals of the same gender, were selected to minimize gender-related variability. Results showed that unruptured cases exhibited higher WSS and TAWSS, along with lower OSI and RRT values patterns consistent with stable flow conditions supporting vascular integrity. In contrast, ruptured cases had lower WSS and TAWSS, coupled with elevated OSI and RRT, suggesting disturbed and oscillatory flow commonly linked to aneurysm wall weakening. ECAP was also higher in ruptured cases, indicating increased endothelial activation under unstable flow. Notably, areas with the highest OSI and RRT often aligned with vortex centers, reinforcing the association between disturbed flow and aneurysm instability. These findings highlight the value of combining multiple hemodynamic parameters for rupture risk assessment. Including RRT and ECAP provides deeper insight into flow endothelium-interactions, offering a stronger basis for evaluating aneurysm stability and guiding treatment decisions.
Molecular Learning Dynamics
Authors: Yaroslav Gusev, Vitaly Vanchurin
Date: 2025-04-14 00:00:00

We apply the physics-learning duality to molecular systems by complementing the physical description of interacting particles with a dual learning description, where each particle is modeled as an agent minimizing a loss function. In the traditional physics framework, the equations of motion are derived from the Lagrangian function, while in the learning framework, the same equations emerge from learning dynamics driven by the agent loss function. The loss function depends on scalar quantities that describe invariant properties of all other agents or particles. To demonstrate this approach, we first infer the loss functions of oxygen and hydrogen directly from a dataset generated by the CP2K physics-based simulation of water molecules. We then employ the loss functions to develop a learning-based simulation of water molecules, which achieves comparable accuracy while being significantly more computationally efficient than standard physics-based simulations.
Constants of motion network revisited
Authors: Wenqi Fang, Chao Chen, Yongkui Yang, Zheng Wang
Date: 2025-04-13 00:00:00

Discovering constants of motion is meaningful in helping understand the dynamical systems, but inevitably needs proficient mathematical skills and keen analytical capabilities. With the prevalence of deep learning, methods employing neural networks, such as Constant Of Motion nETwork (COMET), are promising in handling this scientific problem. Although the COMET method can produce better predictions on dynamics by exploiting the discovered constants of motion, there is still plenty of room to sharpen it. In this paper, we propose a novel neural network architecture, built using the singular-value-decomposition (SVD) technique, and a two-phase training algorithm to improve the performance of COMET. Extensive experiments show that our approach not only retains the advantages of COMET, such as applying to non-Hamiltonian systems and indicating the number of constants of motion, but also can be more lightweight and noise-robust than COMET.
MLPs and KANs for data-driven learning in physical problems: A performance comparison
Authors: Raghav Pant, Sikan Li, Xingjian Li, Hassan Iqbal, Krishna Kumar
Date: 2025-04-15 00:00:00
Tasks: Kolmogorov-Arnold Networks

There is increasing interest in solving partial differential equations (PDEs) by casting them as machine learning problems. Recently, there has been a spike in exploring Kolmogorov-Arnold Networks (KANs) as an alternative to traditional neural networks represented by Multi-Layer Perceptrons (MLPs). While showing promise, their performance advantages in physics-based problems remain largely unexplored. Several critical questions persist: Can KANs capture complex physical dynamics and under what conditions might they outperform traditional architectures? In this work, we present a comparative study of KANs and MLPs for learning physical systems governed by PDEs. We assess their performance when applied in deep operator networks (DeepONet) and graph network-based simulators (GNS), and test them on physical problems that vary significantly in scale and complexity. Drawing inspiration from the Kolmogorov Representation Theorem, we examine the behavior of KANs and MLPs across shallow and deep network architectures. Our results reveal that although KANs do not consistently outperform MLPs when configured as deep neural networks, they demonstrate superior expressiveness in shallow network settings, significantly outpacing MLPs in accuracy over our test cases. This suggests that KANs are a promising choice, offering a balance of efficiency and accuracy in applications involving physical systems.
Dynamical errors in machine learning forecasts
Authors: Zhou Fang, Gianmarco Mengaldo
Date: 2025-04-15 00:00:00
Tasks: Weather Forecasting

In machine learning forecasting, standard error metrics such as mean absolute error (MAE) and mean squared error (MSE) quantify discrepancies between predictions and target values. However, these metrics do not directly evaluate the physical and/or dynamical consistency of forecasts, an increasingly critical concern in scientific and engineering applications. Indeed, a fundamental yet often overlooked question is whether machine learning forecasts preserve the dynamical behavior of the underlying system. Addressing this issue is essential for assessing the fidelity of machine learning models and identifying potential failure modes, particularly in applications where maintaining correct dynamical behavior is crucial. In this work, we investigate the relationship between standard forecasting error metrics, such as MAE and MSE, and the dynamical properties of the underlying system. To achieve this goal, we use two recently developed dynamical indices: the instantaneous dimension ($d$), and the inverse persistence ($\theta$). Our results indicate that larger forecast errors -- e.g., higher MSE -- tend to occur in states with higher $d$ (higher complexity) and higher $\theta$ (lower persistence). To further assess dynamical consistency, we propose error metrics based on the dynamical indices that measure the discrepancy of the forecasted $d$ and $\theta$ versus their correct values. Leveraging these dynamical indices-based metrics, we analyze direct and recursive forecasting strategies for three canonical datasets -- Lorenz, Kuramoto-Sivashinsky equation, and Kolmogorov flow -- as well as a real-world weather forecasting task. Our findings reveal substantial distortions in dynamical properties in ML forecasts, especially for long forecast lead times or long recursive simulations, providing complementary information on ML forecast fidelity that can be used to improve ML models.
Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging
Authors: Mathieu Manni, Dmitry Karpov, K. Joost Batenburg, Sharon Shwartz, Nicola Viganò
Date: 2025-04-14 00:00:00

We present a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction performance for noisy acquisitions among unsupervised methods. We present the supporting mathematical framework and results from theoretical and real data use cases. Self-supervision removes the need for clean reference data while offering strong noise reduction. This provides the necessary tools for addressing signal-to-noise ratio concerns for GI acquisitions in emerging and cutting-edge low-light GI scenarios. Notable examples include micro- and nano-scale x-ray emission imaging, e.g., x-ray fluorescence imaging of dose-sensitive samples. Their applications include in-vivo and in-operando case studies for biological samples and batteries.
A simulation-heuristics dual-process model for intuitive physics
Authors: Shiqian Li, Yuxi Ma, Jiajun Yan, Bo Dai, Yujia Peng, Chi Zhang, Yixin Zhu
Date: 2025-04-13 00:00:00

The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simulation time exceeded a certain boundary. Motivated by these observations, we propose a dual-process framework, Simulation-Heuristics Model (SHM), where intuitive physics employs simulation for short-time simulation but switches to heuristics when simulation becomes costly. By integrating computational methods previously viewed as separate into a unified model, SHM quantitatively captures their switching mechanism. The SHM aligns more precisely with human behavior and demonstrates consistent predictive performance across diverse scenarios, advancing our understanding of the adaptive nature of intuitive physical reasoning.
Virtual domain extension for imposing boundary conditions in flow simulation using pre-trained local neural operator
Authors: Ximeng Ye, Hongyu Li, Zhen-Guo Yan
Date: 2025-04-14 00:00:00

This paper builds up a virtual domain extension (VDE) framework for imposing boundary conditions (BCs) in flow simulation using pre-trained local neural operator (LNO). It creates extended virtual domains to the input function to compensate for the corrosion nature of computational domains during LNO inference, thus turns the implementation of BC into the determination of field values on the extended domain. Several strategies to calculate the field values are proposed and validated in solving numerical examples, including padding operation, direct imposition, pressure symmetry, and optimization by backpropagation, and compared with boundary imposition in traditional solvers. It is found that the large time interval of LNO induces a relatively wide near-boundary domain to be processed, thus imposing BC on only a few nodes near the boundary following the immersed boundary conception in traditional solvers can hardly achieve high accuracy. With appropriate values assigned on the extended virtual domains, VDE can accurately impose BCs and lead to reasonable flow field predictions. This work provides a guidance for imposing BCs reliably in LNO prediction, which could facilitate the reuse of pre-trained LNO in more applications.
Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations
Authors: Yue Li, Lihong Zhang
Date: 2025-04-13 00:00:00

This paper presents an improved approach for preserving sharp interfaces in multiphase Lattice Boltzmann Method (LBM) simulations using Physics-Informed Neural Networks (PINNs). Interface diffusion is a common challenge in multiphase LBM, leading to reduced accuracy in simulating phenomena where interfacial dynamics are critical. We propose a coupled PINN-LBM framework that maintains interface sharpness while preserving the physical accuracy of the simulation. Our approach is validated through droplet simulations, with quantitative metrics measuring interface width, maximum gradient, phase separation, effective interface width, and interface energy. The enhanced visualization techniques employed in this work clearly demonstrate the superior performance of PINN-LBM over standard LBM for multiphase simulations, particularly in maintaining well-defined interfaces throughout the simulation. We provide a comprehensive analysis of the results, showcasing how the neural network integration effectively counteracts numerical diffusion, while maintaining physical consistency with the underlying fluid dynamics.