pwc-archive/papers-with-abstracts huggingface dataset:
Discriminative versus Generative Approaches to Simulation-based Inference
Authors: Benjamin Sluijter, Sascha Diefenbacher, Wahid Bhimji, Benjamin Nachman
Date: 2025-03-11 00:00:00
Tasks: Hyperparameter Optimization, parameter estimation

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihiood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.
Multilevel Generative Samplers for Investigating Critical Phenomena
Authors: Ankur Singha, Elia Cellini, Kim A. Nicoli, Karl Jansen, Stefan Kühn, Shinichi Nakajima
Date: 2025-03-11 00:00:00

Investigating critical phenomena or phase transitions is of high interest in physics and chemistry, for which Monte Carlo (MC) simulations, a crucial tool for numerically analyzing macroscopic properties of given systems, are often hindered by an emerging divergence of correlation length -- known as scale invariance at criticality (SIC) in the renormalization group theory. SIC causes the system to behave the same at any length scale, from which many existing sampling methods suffer: long-range correlations cause critical slowing down in Markov chain Monte Carlo (MCMC), and require intractably large receptive fields for generative samplers. In this paper, we propose a Renormalization-informed Generative Critical Sampler (RiGCS) -- a novel sampler specialized for near-critical systems, where SIC is leveraged as an advantage rather than a nuisance. Specifically, RiGCS builds on MultiLevel Monte Carlo (MLMC) with Heat Bath (HB) algorithms, which perform ancestral sampling from low-resolution to high-resolution lattice configurations with site-wise-independent conditional HB sampling. Although MLMC-HB is highly efficient under exact SIC, it suffers from a low acceptance rate under slight SIC violation. Notably, SIC violation always occurs in finite-size systems, and may induce long-range and higher-order interactions in the renormalized distributions, which are not considered by independent HB samplers. RiGCS enhances MLMC-HB by replacing a part of the conditional HB sampler with generative models that capture those residual interactions and improve the sampling efficiency. Our experiments show that the effective sample size of RiGCS is a few orders of magnitude higher than state-of-the-art generative model baselines in sampling configurations for 128x128 two-dimensional Ising systems.
Symbolic Neural Ordinary Differential Equations
Authors: Xin Li, Chengli Zhao, Xue Zhang, Xiaojun Duan
Date: 2025-03-11 00:00:00

Differential equations are widely used to describe complex dynamical systems with evolving parameters in nature and engineering. Effectively learning a family of maps from the parameter function to the system dynamics is of great significance. In this study, we propose a novel learning framework of symbolic continuous-depth neural networks, termed Symbolic Neural Ordinary Differential Equations (SNODEs), to effectively and accurately learn the underlying dynamics of complex systems. Specifically, our learning framework comprises three stages: initially, pre-training a predefined symbolic neural network via a gradient flow matching strategy; subsequently, fine-tuning this network using Neural ODEs; and finally, constructing a general neural network to capture residuals. In this process, we apply the SNODEs framework to partial differential equation systems through Fourier analysis, achieving resolution-invariant modeling. Moreover, this framework integrates the strengths of symbolism and connectionism, boasting a universal approximation theorem while significantly enhancing interpretability and extrapolation capabilities relative to state-of-the-art baseline methods. We demonstrate this through experiments on several representative complex systems. Therefore, our framework can be further applied to a wide range of scientific problems, such as system bifurcation and control, reconstruction and forecasting, as well as the discovery of new equations.
Redefining Fitness: Evolution as a Dynamic Learning Process
Authors: Luís MA Bettencourt, Brandon J Grandison, Jordan T Kemp
Date: 2025-03-12 00:00:00
Tasks: Learning Theory

Evolution is the process of optimal adaptation of biological populations to their living environments. This is expressed via the concept of fitness, defined as relative reproductive success. However, it has been pointed out that this definition is incomplete and logically circular. To address this issue, several authors have called for new ways to specify fitness explicitly in terms of the relationship between phenotypes and their environment. Here, we show that fitness, defined as the likelihood function that follows from mapping population dynamics to Bayesian learning, provides a general solution to this problem. We show how probabilistic models of fitness can easily be constructed in this way, and how their averages acquire meaning as information. We also show how this approach leads to powerful tools to analyze challenging problems of evolution in variable environments, game theory, and selection in group-structured populations. The approach is general and creates an explicit bridge between population dynamics under selection, statistical learning theory, and emerging models of artificial intelligence.
On the Importance of Clearsky Model in Short-Term Solar Radiation Forecasting
Authors: Cyril Voyant, Milan Despotovic, Gilles Notton, Yves-Marie Saint-Drenan, Mohammed Asloune, Luis Garcia-Gutierrez
Date: 2025-03-06 00:00:00
Tasks: quantile regression, Uncertainty Quantification

Clearsky models are widely used in solar energy for many applications such as quality control, resource assessment, satellite-base irradiance estimation and forecasting. However, their use in forecasting and nowcasting is associated with a number of challenges. Synchronization errors, reliance on the Clearsky index (ratio of the global horizontal irradiance to its cloud-free counterpart) and high sensitivity of the clearsky model to errors in aerosol optical depth at low solar elevation limit their added value in real-time applications. This paper explores the feasibility of short-term forecasting without relying on a clearsky model. We propose a Clearsky-Free forecasting approach using Extreme Learning Machine (ELM) models. ELM learns daily periodicity and local variability directly from raw Global Horizontal Irradiance (GHI) data. It eliminates the need for Clearsky normalization, simplifying the forecasting process and improving scalability. Our approach is a non-linear adaptative statistical method that implicitely learns the irradiance in cloud-free conditions removing the need for an clear-sky model and the related operational issues. Deterministic and probabilistic results are compared to traditional benchmarks, including ARMA with McClear-generated Clearsky data and quantile regression for probabilistic forecasts. ELM matches or outperforms these methods, providing accurate predictions and robust uncertainty quantification. This approach offers a simple, efficient solution for real-time solar forecasting. By overcoming the stationarization process limitations based on usual multiplicative scheme Clearsky models, it provides a flexible and reliable framework for modern energy systems.
Slowing translation to avoid ribosome population extinction and maintain stable allocation at slow growth rates
Authors: Dotan Goberman, Anjan Roy, Rami Pugatch
Date: 2025-03-10 00:00:00

To double the cellular population of ribosomes, a fraction of the active ribosomes is allocated to synthesize ribosomal proteins. Subsequently, these ribosomal proteins enter the ribosome self-assembly process, synthesizing new ribosomes and forming the well-known ribosome autocatalytic subcycle. Neglecting ribosome lifetime and the duration of the self-assembly process, the doubling rate of all cellular biomass can be equated with the fraction of ribosomes allocated to synthesize an essential ribosomal protein times its synthesis rate. However, ribosomes have a finite lifetime, and the assembly process has a finite duration. Furthermore, the number of ribosomes is known to decrease with slow growth rates. The finite lifetime of ribosomes and the decline in their numbers present a challenge in sustaining slow growth solely through controlling the allocation of ribosomes to synthesize more ribosomal proteins. When the number of ribosomes allocated per mRNA of an essential ribosomal protein is approximately one, the resulting fluctuations in the production rate of new ribosomes increase, causing a potential risk that the actual production rate will fall below the ribosome death rate. Thus, in this regime, a significant risk of extinction of the ribosome population emerges. To mitigate this risk, we suggest that the ribosome translation speed is used as an alternative control parameter, which facilitates the maintenance of slow growth rates with a larger ribosome pool. We clarify the observed reduction in translation speed at harsh environments in E. coli and C. Glutamicum, explore other mitigation strategies, and suggest additional falsifiable predictions of our model.
ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals
Authors: Jonas Elsborg, Luca Thiede, Alán Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik
Date: 2025-03-11 00:00:00
Tasks: Computational Efficiency, Position

We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using floating orbitals. Floating orbitals are a long-standing concept in the quantum chemistry community that promises more compact and accurate representations by placing orbitals freely in space, as opposed to centering all orbitals at the position of atoms. Finding the ideal placement of these orbitals requires extensive domain knowledge, though, which thus far has prevented widespread adoption. We solve this in a data-driven manner by training a Cartesian tensor network to predict the orbital positions along with orbital coefficients. This is made possible through a symmetry-breaking mechanism that is used to learn position displacements with lower symmetry than the input molecule while preserving the rotation equivariance of the charge density itself. Inspired by recent successes of Gaussian Splatting in representing densities in space, we are using Gaussian orbitals and predicting their weights and covariance matrices. Our method achieves a state-of-the-art balance between computational efficiency and predictive accuracy on established benchmarks.
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Authors: Carolina Introini, Stefano Riva, J. Nathan Kutz, Antonio Cammi
Date: 2025-03-11 00:00:00
Tasks: Decoder, State Estimation

The Shallow Recurrent Decoder networks are a novel paradigm recently introduced for state estimation, combining sparse observations with high-dimensional model data. This architecture features important advantages compared to standard data-driven methods including: the ability to use only three sensors (even randomly selected) for reconstructing the entire dynamics of a physical system; the ability to train on compressed data spanned by a reduced basis; the ability to measure a single field variable (easy to measure) and reconstruct coupled spatio-temporal fields that are not observable and minimal hyper-parameter tuning. This approach has been verified on different test cases within different fields including nuclear reactors, even though an application to a real experimental facility, adopting the employment of in-situ observed quantities, is missing. This work aims to fill this gap by applying the Shallow Recurrent Decoder architecture to the DYNASTY facility, built at Politecnico di Milano, which studies the natural circulation established by internally heated fluids for Generation IV applications, especially in the case of Circulating Fuel reactors. The RELAP5 code is used to generate the high-fidelity data, and temperature measurements extracted by the facility are used as input for the state estimation. The results of this work will provide a validation of the Shallow Recurrent Decoder architecture to engineering systems, showing the capabilities of this approach to provide and accurate state estimation.
Physics-based AI methodology for Material Parameter Extraction from Optical Data
Authors: M. Koumans, J. L. M. van Mechelen
Date: 2025-03-11 00:00:00
Tasks: object-detection, Object Detection

We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating physics into the neural network. We validate and analyze its performance on simulated transmission spectra at terahertz and infrared frequencies. Compared to traditional model-based approaches, our method is designed to be autonomous, robust, and time-efficient, making it particularly relevant for industrial and societal applications.
Uncertainty Quantification for Multi-fidelity Simulations
Authors: Swapnil Kumar
Date: 2025-03-11 00:00:00
Tasks: Uncertainty Quantification

The work focuses on gathering high-fidelity and low-fidelity numerical simulations data using Nektar++ (Solver based on Applied Mathematics) and XFOIL respectively. The utilization of the higher polynomial distribution in calculating the Coefficient of lift and drag has demonstrated superior accuracy and precision. Further, Co-kriging Data fusion and Adaptive sampling technique has been used to obtain the precise data predictions for the lift and drag within the confined domain without conducting the costly simulations on HPC clusters. This creates a methodology to quantifying uncertainty in computational fluid dynamics by minimizing the required number of samples. To minimize the reliability on high-fidelity numerical simulations in Uncertainty Quantification, a multi-fidelity strategy has been adopted. The effectiveness of the multi-fidelity deep neural network model has been validated through the approximation of benchmark functions across 1-, 32-, and 100-dimensional, encompassing both linear and nonlinear correlations. The surrogate modelling results showed that multi-fidelity deep neural network model has shown excellent approximation capabilities for the test functions and multi-fidelity deep neural network method has outperformed Co-kriging in effectiveness. In addition to that, multi-fidelity deep neural network model is utilized for the simulation of aleatory uncertainty propagation in 1-, 32-, and 100 dimensional function test, considering both uniform and Gaussian distributions for input uncertainties. The results have shown that multi-fidelity deep neural network model has efficiently predicted the probability density distributions of quantities of interest as well as the statistical moments with precision and accuracy. The Co-Kriging model has exhibited limitations when addressing 32-Dimension problems due to the limitation of memory capacity for storage and manipulation.
DISTINGUISH Workflow: A New Paradigm of Dynamic Well Placement Using Generative Machine Learning
Authors: Sergey Alyaev, Kristian Fossum, Hibat Errahmen Djecta, Jan Tveranger, Ahmed H. Elsheikh
Date: 2025-03-11 00:00:00

The real-time process of directional changes while drilling, known as geosteering, is crucial for hydrocarbon extraction and emerging directional drilling applications such as geothermal energy, civil infrastructure, and CO2 storage. The geo-energy industry seeks an automatic geosteering workflow that continually updates the subsurface uncertainties and captures the latest geological understanding given the most recent observations in real-time. We propose "DISTINGUISH": a real-time, AI-driven workflow designed to transform geosteering by integrating Generative Adversarial Networks (GANs) for geological parameterization, ensemble methods for model updating, and global discrete dynamic programming (DDP) optimization for complex decision-making during directional drilling operations. The DISTINGUISH framework relies on offline training of a GAN model to reproduce relevant geology realizations and a Forward Neural Network (FNN) to model Logging-While-Drilling (LWD) tools' response for a given geomodel. This paper introduces a first-of-its-kind workflow that progressively reduces GAN-geomodel uncertainty around and ahead of the drilling bit and adjusts the well plan accordingly. The workflow automatically integrates real-time LWD data with a DDP-based decision support system, enhancing predictive models of geology ahead of drilling and leading to better steering decisions. We present a simple yet representative benchmark case and document the performance target achieved by the DISTINGUISH workflow prototype. This benchmark will be a foundation for future methodological advancements and workflow refinements.
Modifying Range-Doppler geometry frameworks to process Spotlight SAR imagery in Polar Format
Authors: Piyush S. Agram
Date: 2025-03-10 00:00:00

We present a simple method to enable processing of Spotlight Synthetic Aperture Radar (SAR) imagery distributed in Polar Format (PFA) using standard Range-Doppler (RDA) geometry algorithms. Our approach is applicable to PFA SAR images characterized by a constant value of the Center of Aperture (COA) time. We present simplified expressions for forward (image-to-ground) and inverse (ground-to-image) geometry mapping using Sensor Independent Complex Data (SICD) conventions. We discuss simple changes needed to current open source SAR software that implement Range-Doppler algorithms, to enable support within them for Spotlight data distributed in SICD format. We include a proof-of-concept script that utilizes the Python packages sarpy and isce3 to demonstrate the correctness of the proposed approach.
Segmentation-Guided CT Synthesis with Pixel-Wise Conformal Uncertainty Bounds
Authors: David Vallmanya Poch, Yorick Estievenart, Elnura Zhalieva, Sukanya Patra, Mohammad Yaqub, Souhaib Ben Taieb
Date: 2025-03-11 00:00:00
Tasks: Conformal Prediction, Prediction Intervals, Translation, Uncertainty Quantification

Accurate dose calculations in proton therapy rely on high-quality CT images. While planning CTs (pCTs) serve as a reference for dosimetric planning, Cone Beam CT (CBCT) is used throughout Adaptive Radiotherapy (ART) to generate sCTs for improved dose calculations. Despite its lower cost and reduced radiation exposure advantages, CBCT suffers from severe artefacts and poor image quality, making it unsuitable for precise dosimetry. Deep learning-based CBCT-to-CT translation has emerged as a promising approach. Still, existing methods often introduce anatomical inconsistencies and lack reliable uncertainty estimates, limiting their clinical adoption. To bridge this gap, we propose STF-RUE, a novel framework integrating two key components. First, STF, a segmentation-guided CBCT-to-CT translation method that enhances anatomical consistency by leveraging segmentation priors extracted from pCTs. Second, RUE, a conformal prediction method that augments predicted CTs with pixel-wise conformal prediction intervals, providing clinicians with robust reliability indicator. Comprehensive experiments using UNet++ and Fast-DDPM on two benchmark datasets demonstrate that STF-RUE significantly improves translation accuracy, as measured by a novel soft-tissue-focused metric designed for precise dose computation. Additionally, STF-RUE provides better-calibrated uncertainty sets for synthetic CT, reinforcing trust in synthetic CTs. By addressing both anatomical fidelity and uncertainty quantification, STF-RUE marks a crucial step toward safer and more effective adaptive proton therapy. Code is available at https://anonymous.4open.science/r/cbct2ct_translation-B2D9/.
Study on Dynamical Behavior of Coinfection Infectious Disease Model
Authors: Yang Liu
Date: 2025-03-12 00:00:00

This paper conducts research on the established model and presents the main conclusions . Firstly, by separately considering the infectivity of each of the two infectious diseases and the infectivity of the population simultaneously infected with the two infectious diseases, the existence of three types of boundary equilibrium points is determined, as well as the existence of the interior equilibrium point when the parameters are under specific conditions. Then, the stability of the equilibrium points is analyzed. It is concluded that under different parameter conditions, the stability of the disease free equilibrium point can exhibit various scenarios, such as a stable node or a saddle-node, etc. For the boundary equilibrium points, the situation is more intricate,and a cusp may occur. The stability of the interior equilibrium point under specific conditions is also presented. Finally,the degeneracy of the equilibrium points is studied through the bifurcation theory.Mainly, the saddle-node bifurcation occurring at the interior equilibrium point is obtained, and when the infection rate of the first infectious disease, the infection rate of the second infectious disease, and the infection rate of the co-infected population to other populations are selected as bifurcation parameters, a codimension 3 B-T bifurcation is obtained.
Single-Qudit Quantum Neural Networks for Multiclass Classification
Authors: Leandro C. Souza, Renato Portugal
Date: 2025-03-12 00:00:00
Tasks: Classification, Quantum Machine Learning

This paper proposes a single-qudit quantum neural network for multiclass classification, by using the enhanced representational capacity of high-dimensional qudit states. Our design employs an $d$-dimensional unitary operator, where $d$ corresponds to the number of classes, constructed using the Cayley transform of a skew-symmetric matrix, to efficiently encode and process class information. This architecture enables a direct mapping between class labels and quantum measurement outcomes, reducing circuit depth and computational overhead. To optimize network parameters, we introduce a hybrid training approach that combines an extended activation function -- derived from a truncated multivariable Taylor series expansion -- with support vector machine optimization for weight determination. We evaluate our model on the MNIST and EMNIST datasets, demonstrating competitive accuracy while maintaining a compact single-qudit quantum circuit. Our findings highlight the potential of qudit-based QNNs as scalable alternatives to classical deep learning models, particularly for multiclass classification. However, practical implementation remains constrained by current quantum hardware limitations. This research advances quantum machine learning by demonstrating the feasibility of higher-dimensional quantum systems for efficient learning tasks.