pwc-archive/papers-with-abstracts huggingface dataset:
Scaling Physical Reasoning with the PHYSICS Dataset
Authors: Shenghe Zheng, QianJia Cheng, Junchi Yao, Mengsong Wu, Haonan He, Ning Ding, Yu Cheng, Shuyue Hu, Lei Bai, Dongzhan Zhou, Ganqu Cui, Peng Ye
Date: 2025-05-21 00:00:00

Large Language Models (LLMs) have achieved remarkable progress on advanced reasoning tasks such as mathematics and coding competitions. Meanwhile, physics, despite being both reasoning-intensive and essential to real-world understanding, received limited academic and industrial attention. This paper introduces PHYSICS, a dataset containing 16,568 high-quality physics problems spanning subjects and difficulty levels, to facilitate this issue. Specifically, PHYSICS is curated with exercises from over 100 textbooks through a carefully designed pipeline for quality control. It covers five major physics domains: Mechanics, Electromagnetism, Thermodynamics, Optics, and Modern Physics. It also spans a wide range of difficulty levels, from high school to graduate-level physics courses. To utilize the data for improving and evaluating the model's physical reasoning capabilities, we split the dataset into training and test sets, and provide reasoning paths generated by powerful reasoning models for the training data to facilitate model training. In addition, for the evaluation part, we find that existing evaluation frameworks exhibit biases in aspects such as units, simplification, and precision in physics domain. To balance efficiency and accuracy, we introduce a Rule+Model evaluation framework tailored to physics problems. Our evaluations on current state-of-the-art open-source and proprietary models highlight the limitations of current models in handling physics-related tasks. We hope that our dataset and evaluation methodology will jointly advance the development of LLMs in the field of physics.
A novel visual data-based diagnostic approach for estimation of regime transition in pool boiling
Authors: Pranay Nirapure, Ayushman Singh, Srikanth Rangarajan, Bahgat Sammakia
Date: 2025-06-12 00:00:00
Tasks: Diagnostic

This study introduces a novel metric, the Index of Visual Similarity (IVS), to qualitatively characterize boiling heat transfer regimes using only visual data. The IVS is constructed by combining morphological similarity, through SIFT-based feature matching, with physical similarity, via vapor area estimation using Mask R-CNN. High-speed images of pool boiling on two distinct surfaces, polished copper and porous copper foam, are employed to demonstrate the generalizability of the approach. IVS captures critical changes in bubble shape, size, and distribution that correspond to transitions in heat transfer mechanisms. The metric is validated against an equivalent metric, $\Phi$, derived from measured heat transfer coefficients (HTC), showing strong correlation and reliability in detecting boiling regime transitions, including the onset of nucleate boiling and proximity to critical heat flux (CHF). Given experimental limitations in precisely measuring changes in HTC, the sensitivity of IVS to surface superheat is also examined to reinforce the credibility of IVS. IVS thus emerges as a powerful, rapid, and non-intrusive tool for real-time, image-based boiling diagnostics, with promising applications in phase change heat transfer.
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
Authors: Yunfei Huang, David S. Greenberg
Date: 2025-06-05 00:00:00
Tasks: Data Augmentation

Neural PDE surrogates can improve the cost-accuracy tradeoff of classical solvers, but often generalize poorly to new initial conditions and accumulate errors over time. Physical and symmetry constraints have shown promise in closing this performance gap, but existing techniques for imposing these inductive biases are incompatible with the staggered grids commonly used in computational fluid dynamics. Here we introduce novel input and output layers that respect physical laws and symmetries on the staggered grids, and for the first time systematically investigate how these constraints, individually and in combination, affect the accuracy of PDE surrogates. We focus on two challenging problems: shallow water equations with closed boundaries and decaying incompressible turbulence. Compared to strong baselines, symmetries and physical constraints consistently improve performance across tasks, architectures, autoregressive prediction steps, accuracy measures, and network sizes. Symmetries are more effective than physical constraints, but surrogates with both performed best, even compared to baselines with data augmentation or pushforward training, while themselves benefiting from the pushforward trick. Doubly-constrained surrogates also generalize better to initial conditions and durations beyond the range of the training data, and more accurately predict real-world ocean currents.
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
Authors: Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades, Klas Wijk, Aakash Patil, Catherine Gorle, Beverley J McKeon, Hossein Azizpour, Ricardo Vinuesa
Date: 2025-05-30 00:00:00

Rapid urbanization demands accurate and efficient monitoring of turbulent wind patterns to support air quality, climate resilience and infrastructure design. Traditional sparse reconstruction and sensor placement strategies face major accuracy degradations under practical constraints. Here, we introduce Diff-SPORT, a diffusion-based framework for high-fidelity flow reconstruction and optimal sensor placement in urban environments. Diff-SPORT combines a generative diffusion model with a maximum a posteriori (MAP) inference scheme and a Shapley-value attribution framework to propose a scalable and interpretable solution. Compared to traditional numerical methods, Diff-SPORT achieves significant speedups while maintaining both statistical and instantaneous flow fidelity. Our approach offers a modular, zero-shot alternative to retraining-intensive strategies, supporting fast and reliable urban flow monitoring under extreme sparsity. Diff-SPORT paves the way for integrating generative modeling and explainability in sustainable urban intelligence.
Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization?
Authors: Ruihua Chen, Bangyu Wu, Meng Li, Kai Yang
Date: 2025-06-05 00:00:00
Tasks: Deep Learning

Subsurface property neural network reparameterized full waveform inversion (FWI) has emerged as an effective unsupervised learning framework, which can invert stably with an inaccurate starting model. It updates the trainable neural network parameters instead of fine-tuning on the subsurface model directly. There are primarily two ways to embed the prior knowledge of the initial model into neural networks, that is, pretraining and denormalization. Pretraining first regulates the neural networks' parameters by fitting the initial velocity model; Denormalization directly adds the outputs of the network into the initial models without pretraining. In this letter, we systematically investigate the influence of the two ways of initial model incorporation for the neural network reparameterized FWI. We demonstrate that pretraining requires inverting the model perturbation based on a constant velocity value (mean) with a two-stage implementation. It leads to a complex workflow and inconsistency of objective functions in the two-stage process, causing the network parameters to become inactive and lose plasticity. Experimental results demonstrate that denormalization can simplify workflows, accelerate convergence, and enhance inversion accuracy compared with pretraining.
Development of a Photon-Counting Deadtime Noise Model that Extends Dynamic Range and Resolution in Atmospheric Lidar
Authors: Grant J. Kirchhoff, Matthew Hayman, Willem J. Marais, Jeffrey P. Thayer, Rory A. Barton-Grimley
Date: 2025-06-10 00:00:00

This work derives and validates a noise model that encapsulates deadtime of non-paralyzable detectors with random photon arrivals to enable advanced processing, like maximum-likelihood estimation, of high resolution atmospheric lidar profiles while accounting for deadtime bias. This estimator was validated across a wide dynamic range at high resolution (4 millimeters in range, 17 milliseconds in time). Experiments demonstrate that the noise model outperforms the current state-of-the-art for very short time-of-flight (2 nanoseconds) and extended targets (1 microsecond). The proposed noise model also produces accurate deadtime correction for very short integration times. This work sets the foundation for further study into accurate retrievals of high flux and dynamic atmospheric features, e.g., clouds and aerosol layers.
Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction
Authors: George Webber, Alexander Hammers, Andrew P. King, Andrew J. Reader
Date: 2025-06-04 00:00:00
Tasks: Anatomy, Image Reconstruction, Image Registration

Recent work has shown improved lesion detectability and flexibility to reconstruction hyperparameters (e.g. scanner geometry or dose level) when PET images are reconstructed by leveraging pre-trained diffusion models. Such methods train a diffusion model (without sinogram data) on high-quality, but still noisy, PET images. In this work, we propose a simple method for generating subject-specific PET images from a dataset of multi-subject PET-MR scans, synthesizing "pseudo-PET" images by transforming between different patients' anatomy using image registration. The images we synthesize retain information from the subject's MR scan, leading to higher resolution and the retention of anatomical features compared to the original set of PET images. With simulated and real [$^{18}$F]FDG datasets, we show that pre-training a personalized diffusion model with subject-specific "pseudo-PET" images improves reconstruction accuracy with low-count data. In particular, the method shows promise in combining information from a guidance MR scan without overly imposing anatomical features, demonstrating an improved trade-off between reconstructing PET-unique image features versus features present in both PET and MR. We believe this approach for generating and utilizing synthetic data has further applications to medical imaging tasks, particularly because patient-specific PET images can be generated without resorting to generative deep learning or large training datasets.
Analytical Reconstruction of Periodically Deformed Objects in Time-resolved CT
Authors: Qianwei Qu, Christian M. Schlepütz, Marco Stampanoni
Date: 2025-06-04 00:00:00

Time-resolved CT is an advanced measurement technique that has been widely used to observe dynamic objects, including periodically varying structures such as hearts, lungs, or hearing structures. To reconstruct these objects from CT projections, a common approach is to divide the projections into several collections based on their motion phases and perform reconstruction within each collection, assuming they originate from a static object. This describes the gating-based method, which is the standard approach for time-periodic reconstruction. However, the gating-based reconstruction algorithm only utilizes a limited subset of projections within each collection and ignores the correlation between different collections, leading to inefficient use of the radiation dose. To address this issue, we propose two analytical reconstruction pipelines in this paper, and validate them with experimental data captured using tomographic synchrotron microscopy. We demonstrate that our approaches significantly reduce random noise in the reconstructed images without blurring the sharp features of the observed objects. Equivalently, our methods can achieve the same reconstruction quality as gating-based methods but with a lower radiation dose. Our code is available at github.com/PeriodRecon.
A combined Machine Learning and Finite Element Modelling tool for the surgical planning of craniosynostosis correction
Authors: Itxasne Antúnez Sáenz, Ane Alberdi Aramendi, David Dunaway, Juling Ong, Lara Deliège, Amparo Sáenz, Anita Ahmadi Birjandi, Noor UI Owase Jeelani, Silvia Schievano, Alessandro Borghi
Date: 2025-06-02 00:00:00
Tasks: Computed Tomography (CT)

Craniosynostosis is a medical condition that affects the growth of babies' heads, caused by an early fusion of cranial sutures. In recent decades, surgical treatments for craniosynostosis have significantly improved, leading to reduced invasiveness, faster recovery, and less blood loss. At Great Ormond Street Hospital (GOSH), the main surgical treatment for patients diagnosed with sagittal craniosynostosis (SC) is spring assisted cranioplasty (SAC). This procedure involves a 15x15 mm2 osteotomy, where two springs are inserted to induce distraction. Despite the numerous advantages of this surgical technique for patients, the outcome remains unpredictable due to the lack of efficient preoperative planning tools. The surgeon's experience and the baby's age are currently relied upon to determine the osteotomy location and spring selection. Previous tools for predicting the surgical outcome of SC relied on finite element modeling (FEM), which involved computed tomography (CT) imaging and required engineering expertise and lengthy calculations. The main goal of this research is to develop a real-time prediction tool for the surgical outcome of patients, eliminating the need for CT scans to minimise radiation exposure during preoperative planning. The proposed methodology involves creating personalised synthetic skulls based on three-dimensional (3D) photographs, incorporating population average values of suture location, skull thickness, and soft tissue properties. A machine learning (ML) surrogate model is employed to achieve the desired surgical outcome. The resulting multi-output support vector regressor model achieves a R2 metric of 0.95 and MSE and MAE below 0.13. Furthermore, in the future, this model could not only simulate various surgical scenarios but also provide optimal parameters for achieving a maximum cranial index (CI).
Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model
Authors: Jiawen Li, Jiang Guo, Yuanzhe Li, Zetian Mao, Jiaxing Shen, Tashi Xu, Diptesh Das, Jinming He, Run Hu, Yaerim Lee, Koji Tsuda, Junichiro Shiomi
Date: 2025-06-08 00:00:00

Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention. However, the highly nonlinear relationship between the metamaterial structures and optical behaviour, coupled with fabrication difficulties, poses challenges for using machine learning to design and manufacture complex metamaterials. Herein, we propose a general framework that implements customised spectrum-to-shape and size parameters to address one-to-many metamaterial inverse design problems using conditional diffusion models. Our method exhibits superior spectral prediction accuracy, generates a diverse range of patterns compared to other typical generative models, and offers valuable prior knowledge for manufacturing through the subsequent analysis of the diverse generated results, thereby facilitating the experimental fabrication of metamaterial designs. We demonstrate the efficacy of the proposed method by successfully designing and fabricating a free-form metamaterial with a tailored selective emission spectrum for thermal camouflage applications.
Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping
Authors: Weijian Zhang, Hashan K. Weerasooriya, Stanley Chan
Date: 2025-05-29 00:00:00
Tasks: Depth Estimation

Efficient simulation of photon registrations in single-photon LiDAR (SPL) is essential for applications such as depth estimation under high-flux conditions, where hardware dead time significantly distorts photon measurements. However, the conventional wisdom is computationally intensive due to their inherently sequential, photon-by-photon processing. In this paper, we propose a learning-based framework that accelerates the simulation process by modeling the photon count and directly predicting the photon registration probability density function (PDF) using an autoencoder (AE). Our method achieves high accuracy in estimating both the total number of registered photons and their temporal distribution, while substantially reducing simulation time. Extensive experiments validate the effectiveness and efficiency of our approach, highlighting its potential to enable fast and accurate SPL simulations for data-intensive imaging tasks in the high-flux regime.
A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics
Authors: Shirui Zhou, Jiying Yan, Junfang Tian, Tao Wang, Yongfu Li, Shiquan Zhong
Date: 2025-06-06 00:00:00
Tasks: Dynamic Time Warping

A fundamental challenge in car-following modeling lies in accurately representing the multi-scale complexity of driving behaviors, particularly the intra-driver heterogeneity where a single driver's actions fluctuate dynamically under varying conditions. While existing models, both conventional and data-driven, address behavioral heterogeneity to some extent, they often emphasize inter-driver heterogeneity or rely on simplified assumptions, limiting their ability to capture the dynamic heterogeneity of a single driver under different driving conditions. To address this gap, we propose a novel data-driven car-following framework that systematically embeds discrete driving regimes (e.g., steady-state following, acceleration, cruising) into vehicular motion predictions. Leveraging high-resolution traffic trajectory datasets, the proposed hybrid deep learning architecture combines Gated Recurrent Units for discrete driving regime classification with Long Short-Term Memory networks for continuous kinematic prediction, unifying discrete decision-making processes and continuous vehicular dynamics to comprehensively represent inter- and intra-driver heterogeneity. Driving regimes are identified using a bottom-up segmentation algorithm and Dynamic Time Warping, ensuring robust characterization of behavioral states across diverse traffic scenarios. Comparative analyses demonstrate that the framework significantly reduces prediction errors for acceleration (maximum MSE improvement reached 58.47\%), speed, and spacing metrics while reproducing critical traffic phenomena, such as stop-and-go wave propagation and oscillatory dynamics.
Genotype networks drive oscillating endemicity and epidemic trajectories in viral evolution
Authors: Santiago Lamata-Otín, Octavian C. Rotita-Ion, Alex Arenas, David Soriano-Paños, Jesús Gómez-Gardeñes
Date: 2025-06-03 00:00:00

Rapidly evolving viruses use antigenic drift as a key mechanism to evade host immunity and persist in real populations. While traditional models of antigenic drift and epidemic spread rely on low-dimensional antigenic spaces, genomic surveillance data reveal that viral evolution produces complex antigenic genotype networks with hierarchical modular structures. In this study, we present an eco-evolutionary framework in which viral evolution and population immunity dynamics are shaped by the structure of antigenic genotype networks. Using synthetic networks, we demonstrate that network topology alone can drive transitions between stable endemic states and recurrent seasonal epidemics. Furthermore, our results show how the integration of the genotype network of the H3N2 influenza in our model allows for estimating the emergence times of various haplotypes resulting from its evolution. Our findings underscore the critical role of the topology of genotype networks in shaping epidemic behavior and, besides, provide a robust framework for integrating real-world genomic data into predictive epidemic models.
Cost of institutional incentives for promoting cooperation in $2\times2$ games and collective risk games
Authors: M. H. Duong, C. M. Durbac, T. A. Han
Date: 2025-06-03 00:00:00

Prosocial behaviours, which appear to contradict Darwinian principles of individual payoff maximisation, have been extensively studied across multiple disciplines. Cooperation, requiring a personal cost for collective benefits, is widespread in nature and has been explained through mechanisms such as kin selection, direct and indirect reciprocity, and network reciprocity. Institutional incentives, which reward cooperation and punish anti-social behaviour, offer a promising approach to fostering cooperation in groups of self-interested individuals. This study investigates the behaviour of the cost functions associated with these types of interventions, using both analytical and numerical methods. Focusing on reward, punishment, and hybrid schemes, we analyse their associated cost functions under evolutionary dynamics governed by Fermi's rule, exploring their asymptotic behaviour in the limits of neutral drift and strong selection. Our analysis focuses on two game types: General $2\times2$ Games (with particular attention paid to the cooperative and defective Prisoner's Dilemma) and the Collective Risk Game. In addition to deriving key analytical results, we use numerical simulations to study how parameters such as the intensity of selection affect the behaviour of the aforementioned incentive cost functions.
OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open Data
Authors: Jinwei Zeng, Yu Liu, Guozhen Zhang, Jingtao Ding, Yuming Lin, Jian Yuan, Yong Li
Date: 2025-06-03 00:00:00
Tasks: Contrastive Learning

Accurately estimating high-resolution carbon emissions is crucial for effective emission governance and mitigation planning. While conventional methods for precise carbon accounting are hindered by substantial data collection efforts, the rise of open data and advanced learning techniques offers a promising solution. Once an open data-based prediction model is developed and trained, it can easily infer emissions for new areas based on available open data. To address this, we incorporate two modalities of open data, satellite images and point-of-interest (POI) data, to predict high-resolution urban carbon emissions, with satellite images providing macroscopic and static and POI data offering fine-grained and relatively dynamic functionality information. However, estimating high-resolution carbon emissions presents two significant challenges: the intertwined and implicit effects of various functionalities on carbon emissions, and the complex spatial contiguity correlations that give rise to the agglomeration effect. Our model, OpenCarbon, features two major designs that target the challenges: a cross-modality information extraction and fusion module to extract complementary functionality information from two modules and model their interactions, and a neighborhood-informed aggregation module to capture the spatial contiguity correlations. Extensive experiments demonstrate our model's superiority, with a significant performance gain of 26.6\% on R2. Further generalizability tests and case studies also show OpenCarbon's capacity to capture the intrinsic relation between urban functionalities and carbon emissions, validating its potential to empower efficient carbon governance and targeted carbon mitigation planning. Codes and data are available: https://github.com/JinweiZzz/OpenCarbon.