Physics-Informed ML

ML: Machine Learning

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60 papers in the last four weeks, up 131% on the four weeks before. 0.6% of all new papers.

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From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
May 19, 2026Jianan Yang, Yiran Wang, Shuai Li +3PDE Solving Curriculum Learning
Xi’an Jiaotong University, Xi’an, China · College of International Business, Zhejiang Yuexiu University, Shaoxing, China · Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart Society, Shaoxing, China +1
Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
May 18, 2026Dong Li, Yapeng Cao, Shuai Huang +4Physics-Informed ML
a PhD, Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA · b PhD, State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Laboratoire Navier/CERMES, École Nationale des Ponts et Chaussées, Institut Polytechnique de Paris, 77455 Marne-la-Vallée cedex 2, France · c PhD, State Key Laboratory of Tunnel Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China +3
PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics
May 18, 2026Xueyu Luan, Chenwei ShiDynamical Systems Reinforcement Learning
School of Electronic and Information Engineering Tongji University Shanghai 200092, China · Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai 200092, China
UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
May 18, 2026Robson W. S. Pessoa, Julien Amblard, Alessandra Russo +1Multivariate Time Series Forecasting Industrial Anomaly Detection
aDepartment of Chemical Engineering, Norwegian University of Science and Technology2026 (NTNU), Trondheim, Norway · bDepartment of Computing, Imperial College London, London, United Kingdom
Physics-informed convolutional neural networks for fluid flow through porous media
May 18, 2026Rafał Topolnicki, Paweł Dłotko, Maciej MatykaPDE Surrogate Modeling Physics-Informed ML
Dioscuri Center in Topological Data Analysis, Institute of Mathematics, Polish Academy of Sciences, ul. ´Sniadeckich 8, 00-656, Warsaw, Poland · Institute of Experimental Physics, Faculty of Physics and Astronomy, University of Wrocław, pl. M. Borna 9, 50-204, Wrocław, Poland · Institute of Theoretical Physics, Faculty of Physics and Astronomy, University of Wrocław, pl. M. Borna 9, Wrocław, 50-204, Poland +1
Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting
May 17, 2026Chitraksh Singh, Monisha Dhanraj, Akram SheriffTransformer Attention Physics-Informed ML
Frondeur Labs · Cisco Systems
Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins
May 16, 2026Yasar Yanik, Himadri Basu, Ricardo G. Sanfelice +1Flow Matching Kalman Filtering
Department of Applied Mathematics, University of California, Santa Cruz, CA 95064, USA · Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA
PULSE: Generative Phase Evolution for Non-Stationary Time Series Forecasting
May 16, 2026Yangyou Liu, Zezhi Shao, Xinyu Chen +3OOD Generalization Time Series Generation
College of Computer Science, Sichuan University, Chengdu, China · Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Institute of Artificial Intelligence, University of Central Florida, Orlando, USA +1
Identify Then Project: Contrastive Learning of Latent Dynamics from Partial Observations with Port-Hamiltonian Structure
May 15, 2026Peilun Li, Kaiyuan Tan, Daniel Moyer +1Contrastive Learning Latent Dynamics Modeling
Department of Computer Science Vanderbilt University Nashville, TN 37235
GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical Systems Physics-Informed Generative Modeling
School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK
Hypothesis-driven construction of mesoscopic dynamics
May 15, 2026Zhuoyuan Li, Aiqing Zhu, Qianxiao LiLatent Dynamics Modeling Nonlinear System Identification
Institute for Functional Intelligent Materials, National University of Singapore · Department of Mathematics, National University of Singapore
Variational Autoregressive Networks with probability priors
May 15, 2026Piotr Białas, Piotr Korcyl, Tomasz Stebel +1Autoregressive Generation Physics-Informed ML
Institute of Applied Computer Science, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland · Institute of Theoretical Physics, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland · Doctoral School of Exact and Natural Sciences, Jagiellonian University, ul. Łojasiewicza 11, 30-348, Kraków, Poland
When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
May 15, 2026Yuan-dong Cao, Chi Chiu SO, Jun-Min Wang +1Adversarial Training PDE Surrogate Modeling
School of Mathematics and Statistics, Beijing Institute of Technology, China · Department of Computer Science & UCL AI Centre, University College London, UK · School of Professional Education and Executive Development The Hong Kong Polytechnic University, China
Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation
May 14, 2026Xujia Chen, Xinyue Hu, Letian Chen +2PDE Solving Curriculum Learning
Department of Automation, Tsinghua University, Beijing 100084, P.R. China
Training-Time Optical Priors for Wireless Capsule Endoscopy Classification: Hemoglobin-Aware Input Fusion with Cross-Vendor Evaluation
May 14, 2026Chengshuai Yang, Lei Xing, Keyaan Zawad Alam +4Endoscopy Medical Image Classification
Department of Biomedical Engineering, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, 75390, TX, USA · Department of Radiation Oncology, Stanford University, Stanford, 94305, CA, USA · Coppell High School, Coppell, TX, USA +4
Randomized Atomic Feature Models for Physics-Informed Identification of Dynamic Systems
May 14, 2026Rajiv Singh, Mario Sznaier, Lennart LjungRandom Feature Methods System Identification
∗The MathWorks Inc., 1 Apple Hill Drive, Natick, MA 01760 USA · ∗∗ECE Dept., Northeastern University, Boston, MA 02115, USA · ∗∗∗Div. of Automatic Control, Linköping University, Sweden
Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
May 13, 2026Lingfei Kong, Haoran MaMedical Image Analysis Longitudinal Medical Image Analysis
Department of Mathematics, Vanderbilt University, Nashville, TN, USA · John A. Paulson School of Engineering and Applied Science, Harvard University, Allston, MA, USA
Physics-Guided Concentration Inference from Resistance Transients in a Mixed-Phase SnO-SnO $_2$ Carbon Monoxide Sensor with p-n Switching
May 13, 2026Sani Biswas, Preetam Singh, Amit Kumar GangwarPhysics-Informed ML
Centro de Modelamiento Matem´atico, Universidad de Chile & IRL 2807 - CNRS, Santiago, Chile · CSIR-National Physical Laboratory, Dr. K.S. Krishnan Marg, New Delhi, 110012, India · Department of Chemical Engineering, Biotechnology and Materials, FCFM, Universidad de Chile, Santiago, Chile and ANID - Millenium Science Initiative, Millenium Nuclei of Advanced MXenes for Sustainable Applications (AMXSA), Santiago, Chile
MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
May 13, 2026Lake Yang, Antonio Malpica-Morales, Frank Ioannis Papadakis Wood +1Neural Network Generalization System Identification
Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
Unified generalization analysis for physics informed neural networks
May 13, 2026Yuka Hashimoto, Tomoharu IwataNeural Network Generalization Physics-Informed ML
1. NTT, Inc. · 2. RIKEN AIP
StampFormer: A Physics-Guided Material-Geometry-Coupled Multimodal Model for Rapid Prediction of Physical Fields in Sheet Metal Stamping
May 13, 2026Jiajie Luo, Mohamed Mohamed, Osama Hassan +8Neural Surrogate Modeling Surrogate Modeling
Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots
May 12, 2026Navid Feizi, Filipe C. Pedrosa, Rajni V. Patel +1Robotics Physics-Informed ML
Canadian Surgical Technologies and Advanced Robotics (CSTAR), London Health Sciences Centre (LHSC), and with the Department of Electrical and Computer Engineering, Western University, London, Ontario, Canada
Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
May 12, 2026Maximino Linares, Guillaume Doras, Thomas HélieDynamical Systems Nonlinear System Identification
1IRCAM, 1, place Igor-Stravinsky, 75004, Paris, France · 2Sorbonne Université, 4, place Jussieu, 75005, Paris, France · 3CNRS, 3 rue Michel-Ange, 75016, Paris, France
OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
May 12, 2026Sanah Suri, Kieran Ringel, Maike SonnewaldConcept Bottleneck Models Mechanistic Interpretability
Department of Computer Science University of California, Davis Davis, CA 95616 · University of Washington, Seattle, WA, USA. · NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA.
Bin Latent Transformer (BiLT): A shift-invariant autoencoder for calibration-free spectral unmixing of turbid media
May 12, 2026Martin HohmannAutoencoders Transformer Attention
Institute of Photonic Technologies (LPT), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Konrad-Zuse-Straße 3/5, 91052 Erlangen, Germany
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
May 11, 2026Archie J. Huang, Dongdong Wang, Shaurya Agarwal +3Ensemble Learning Traffic Flow Estimation
Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada · Urban Artificial Intelligence Laboratory, University of Florida, Gainesville, USA · Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, USA
GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
May 11, 2026Juan Diego Toscano, Zhaojie Chai, George Em KarniadakisAI Agents for Scientific Discovery Automated Algorithm Discovery
Division of Applied Mathematics, Brown University, Providence, 02912, RI, USA
CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations
May 11, 2026Nengbo Lu, Minghua PanDynamic Scene Reconstruction Video Prediction
Guilin University of Electronic Technology · Guilin, China
VeloGauss: Learning Physically Consistent Gaussian Velocity Fields from Videos
May 11, 2026Nengbo Lu, Bin Zhao3D Gaussian Splatting Dynamic Scene Reconstruction
Guangxi Key Laboratory of Robot Intelligent Perception and Control, School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, 541004, China
PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay
May 11, 2026Zetao YangEfficient Neural Network Inference Reward Shaping
School of Mathematics and Statistics Beijing Institute of Technology Beijing, China

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From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models

Jianan Yang, Yiran Wang, Shuai Li, Fujun Cao+2

Xi’an Jiaotong University, Xi’an, China · College of International Business, Zhejiang Yuexiu University, Shaoxing, China · Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart Society, Shaoxing, China+1

PDE Solving Curriculum Learning Physics-Informed ML

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Physics-informed neural networks (PINNs) offer a mesh-free framework for solving partial differential equations (PDEs), yet training often suffers from gradient pathologies, spectral bias, and poor convergence, especially for problems with strong nonlinearity, sharp gradients, or multiscale features. We propose the Curriculum-Guided Gaussian Mixture Physics-Informed Neural Network (CGMPINN), which integrates Gaussian mixture modeling with dynamic curriculum learning. Specifically, a GMM is periodically fitted to the PDE residual distribution to quantify spatially varying learning difficulty. A smooth curriculum schedule progressively shifts training focus from easy to harder regions, while precision-based variance modulation suppresses unreliable clusters during early optimization. This dual curriculum is governed by a shared curriculum parameter and can be combined with self-adaptive loss balancing. We further establish theoretical guarantees, including sublinear convergence of the gradient norm for the induced time-varying loss, uniform equivalence between the curriculum-weighted and standard PDE losses, and a generalization bound with an explicit weighting-induced bias characterization. Experiments on six benchmark PDEs spanning elliptic, parabolic, hyperbolic, advection-dominated, and nonlinear reaction-diffusion types show that CGMPINN consistently achieves the lowest relative

L_2

and maximum absolute errors among all compared methods, reducing relative

L_2

error by up to 97.8% over the standard PINN at comparable cost. Our code is publicly available at https://github.com/Mathematics-Yang/CGMPINN.

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