Physics-Informed ML

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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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Physics-Constrained Neural Surrogate for Domain Growth Prediction in Systems with Conserved Kinetics
Jun 9, 2026Vijay Yadav, Pallvi Pandey, Madhu Priya +2Dynamical Systems PDE Surrogate Modeling
Department of Physics, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342 030, India · Department of Physics, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835 215, India · Department of Physics, Central University of Rajasthan, Ajmer, Rajasthan 305 817, India
PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models
Jun 9, 2026Emma Kasteleyn, Timo Maier, Axel Lauer +3Forecasting Benchmarks Physics-Informed ML
University of Amsterdam, Amsterdam, The Netherlands · Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany +1
PF-Trans: Physics-Embedded Frequency-Aware Transformer for Spectral Reconstruction
Jun 9, 2026Yuzhe Gui, Tianzhu Liu, Yanfeng Gu +1Frequency-Domain Feature Learning Hyperspectral Imaging
Harbin Institute of Technology 150001 Harbin, China
Loss Landscape Diagnosis for Gradient-Based Gray-Scott System Inversion: Disentangling the Roles of PINN Components
Jun 9, 2026Yan YangDynamical Systems Parameter Estimation
Independent Researcher.
Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow
Jun 8, 2026Daniel Cieslak, Andrzej CzyzewskiPDE Surrogate Modeling Physics-Informed ML
Gdańsk University of Technology, Gdańsk, Poland
Operator learning for solving Fokker-Planck equations with various initial conditions
Jun 8, 2026Li Zeng, Xiaoliang Wan, Yaobin Wang +2PDE Solving PDE Operator Learning
School of Mathematics and Statistics, Fuzhou University, Fuzhou, China · Department of Mathematics and Center for Computation and Technology, Louisiana State University, Baton Rouge 70803, USA · Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai 519087, China. +2
Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design
Jun 8, 2026Yijie Li, Jiahao Xu, Ching-Chih Tsao +2Materials Science Physics-Informed ML
National University of Singapore, Singapore · UT Austin USA
Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction
Jun 8, 2026Yaser Mike Banad, Sarah SharifManufacturing Constrained Generative Modeling
School of Electrical and Computer Engineering, University of Oklahoma · 3Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory · 4Material Science and Engineering Program, University of Oklahoma, Norman, OK 73019 USA +1
Physics-Guided Dual Decoding and Spectral Supervision for Global 3D Hydrometeor Prediction
Jun 7, 2026Dandan Chen, Yaqiang WangPhysics-Informed ML Spatiotemporal Forecasting
GPT-Micro: A large language paradigm for accelerated, inexpensive, and thermodynamics-consistent discovery of constitutive models in manufacturing
Jun 6, 2026Soumik Dutta, Kiarash Naghavi Khanghah, Sania Shree +4Manufacturing Materials Property Prediction
Department of Mechanical and Aerospace Engineering, Rutgers University, USA · Department of Mechanical, Aerospace & Manufacturing Engineering, University of Connecticut, USA · Edison Welding Institute, USA +2
Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion
Jun 6, 2026Shreesh Bhattarai, Harish Chandra BhandariPDE Solving Physics-Informed ML
Department of Mathematics, Kathmandu University Dhulikhel, Nepal
KFTD: Koopman-Fourier Time-Differentiable Network for Continuous Ocean Spatiotemporal Forecasting
Jun 6, 2026Qinghui Chen, Zekai Zhang, Hailong Liu +2Koopman Operator Learning Physics-Informed ML
Shandong University School of Control Science and Engineering Jinan, China · Laoshan Laboratory Qingdao, China · Chinese Academy of sciences Institute of Atmospheric Physics Beijing, China +1
Physiologically Constrained Musculoskeletal Neural Network for Multi-DoF Joint Kinematics Estimation from Partially Observed sEMG
Jun 5, 2026Wending Heng, Mingming Zhang, Glen Cooper +1Physics-Informed ML
University of Manchester · 2Southern University of Science and Technology
No-Harm Physics-Informed Inverse Learning with Residual-Calibrated Uncertainty
Jun 5, 2026Ronald KatendeUncertainty Quantification Physics-Informed ML
Department of Mathematics Kabale University Kikungiri Hill, Katuna Road, Kabale, Uganda
Decision-Aware Evaluation of Physics-Informed Surrogates
Jun 5, 2026Daniel Cieślak, Andrzej CzyżewskiSurrogate Modeling Benchmark Design
Gdańsk University of Technology, Gdańsk, Poland
Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
Jun 5, 2026Ravisha Rupasinghe, Rajith Vidanaarachchi, Asela Hevapathige +3Physics-Informed ML
AI, Optimisation and Pattern Recognition Lab, Department of Mechanical Engineering, University of Melbourne, Melbourne, Victoria, Australia · Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images
Jun 5, 2026Yifei Yin, Xiaogang Yu, Hao Shi +2Remote Sensing Image Understanding Physics-Informed ML
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China, and with the National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing · Beijing Institute of Remote Sensing Information, Beijing, China
DAS-PINNs for high-dimensional partial differential equations: extending deep adaptive sampling to spacetime domains
Jun 4, 2026Anshima Singh, David J. SilvesterPDE Solving Adaptive Sampling
Department of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK
Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
Jun 4, 2026Mahmoud Elhadidy, Siva Viknesh, Roshan M. D'Souza +1Differentiable Physics Physics-Informed ML
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA · Department of Mechanical Engineering, University of Wisconsin–Milwaukee, Milwaukee, WI, USA +1
Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks
Jun 4, 2026Cornelius Otchere, Michael ShieldsIntrinsic Dimensionality Physics-Informed ML
Department of Civil and Systems Engineering, Johns Hopkins University, Baltimore, Maryland, USA
On the training of physics-informed neural operators for solving parametric partial differential equations
Jun 4, 2026Nanxi Chen, Chuanjie Cui, Airong Chen +2Neural Network Optimization Gradient Interference
College of Civil Engineering, Tongji University, Shanghai 200092 · Department of Engineering Science, University of Oxford, Oxford OX1 3PJ · Institute for Foundations of Data Science, Yale University, New Haven, CT 06520
Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
Jun 4, 2026Shah Pallav Dhanendrakumar, Saikat Pal, Sitikantha RoyNeural Surrogate Modeling Differentiable Programming
Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi-110016, India · Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi-110016, India
Inverse Design of Realizable Metasurface based Absorbers using Improved Conditioning and Diversity Enhanced Progressively Growing GANs
Jun 4, 2026Vineetha Joy, Mohammad Abdullah, Pramit Pal +3Physics-Informed Generative Modeling Physics-Informed ML
Centre for Electromagnetics, CSIR-National Aerospace Laboratories, Kodihalli, Bangalore 560017 · Birla Institute of Technology and Science, Pilani, Rajasthan 333031 · Indian Institute of Technology, Bombay, Maharashtra 400076
Curvature-aware dynamic precision approach for physics-informed neural networks
Jun 3, 2026Yingjie Shao, Ioannis N. Athanasiadis, George van Voorn +1Neural PDE Solvers Neural Network Optimization
Mathematical & Statistical Methods Group (Biometris), Wageningen University & Research, Wageningen, 6700AA, The Netherlands · Artificial Intelligence Group, Wageningen University & Research, Wageningen, 6700AA, The Netherlands
Loss-Conditional PINNs for Parametric PDE Families
Jun 3, 2026Anna Lazareva, Alexander TarakanovPDE Solving PDE Operator Learning
Faculty of Computer Science HSE University, Moscow, Russia · VK and HSE University Moscow, Russia
Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
Jun 3, 2026Dong Li, Yapeng Cao, Haiping Zhao +1Physics-Informed ML
PhD, Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA · 2* 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 +1
PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems
Jun 2, 2026Ismail Hassaballa, Mircea LazarNonlinear System Identification Physics-Informed ML
Eindhoven University of Technology Eindhoven, Netherlands
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Jun 2, 2026Cheng Jiang, Sitian Qian, Kevin Pedro +3Physics-Informed Generative Modeling Flow Matching
School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, United Kingdom · Department of Physics, University of Wisconsin-Madison, Madison, WI 53706, USA · Fermi National Accelerator Laboratory, Batavia, IL 60510, USA +2
Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery
Jun 2, 2026Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-BeniFlood Inundation Mapping Physics-Informed ML
Built Environment Department, College of Science and Technology, North Carolina A&T State University, Greensboro, NC 27405, USA · United Nations University Institute for Water, Environment and Health, Richmond Hill, ON, Canada
Physics-Informed Machine Learning for Short-Term Flood Prediction
Jun 2, 2026Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-BeniLong Short-Term Memory Networks Physics-Informed ML
North Carolina A&T State University · Greensboro, NC 27407, USA

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Physics-Constrained Neural Surrogate for Domain Growth Prediction in Systems with Conserved Kinetics

Vijay Yadav, Pallvi Pandey, Madhu Priya, Manish Dev Shrimali+1

Department of Physics, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan 342 030, India · Department of Physics, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835 215, India · Department of Physics, Central University of Rajasthan, Ajmer, Rajasthan 305 817, India

Dynamical Systems PDE Surrogate Modeling Physics-Informed ML

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The spatiotemporal evolution of many physical, chemical, and biological systems is described by nonlinear partial differential equations (PDEs). Recently, deep neural network-based surrogate models have emerged as efficient alternatives to computationally expensive numerical PDE solvers. In this work, we propose a physics-constrained deep neural network as a surrogate model to learn the microstructural evolution of a binary mixture, in which conservation of the order parameter is imposed directly on the network output as a hard constraint. We train the model to accurately predict the time-evolution of phase separation in binary mixtures governed by the Cahn-Hilliard equation. We show that predictions from our trained surrogate model remain stable and accurate over long-time rollouts for both critical and off-critical mixtures and preserve the mixture composition throughout evolution. In contrast, a variant in which conservation is enforced only via a penalty term in the loss function drifts away from the initial composition and significantly loses predictive accuracy over the same rollout. This establishes that the hard constraint is essential for long-time stability and order parameter conservation. We also show that our model accurately captures the growth of domain size and is consistent with the Lifshitz-Slyozov domain-growth law. These results demonstrate the effectiveness of the proposed framework for modeling systems with conserved kinetics, and the construction extends directly to other systems with conserved quantities.

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