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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Latest papers 561

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PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
Sep 23, 2026Kevin Lee, Alison J. MarchInterpretable Anomaly Detection Convolutional Autoencoder
NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA
SHRAV: State-Hypothesis-Reason-Action-Verify Framework for Physical Modeling and Inverse Design
Sep 23, 2026Ziheng Guo, Yang BuPhysics-Informed ML Inverse Design
ZhangJiang Laboratory
PhyMo: A Physical-Field Modality for Multimodal AI4Physics
Sep 23, 2026Henan Sun, Haitao Hu, Jin Liu +4AI for Science Cross-Modal Representation Learning
The Hong Kong University of Science and Technology (Guangzhou) · Huawei Noah’s Ark Lab · Tencent HY +1
Data-driven discrete-time deep recurrent neural network-based modeling for dissipative systems
Sep 23, 2026Tuan Luong, Hyungpil MoonDynamical Systems Recurrent Neural Networks
Department of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
Untangling the Geometry and Speed for RF Sensing Spectrograms
Sep 22, 2026Mert Torun, Darius Cuenca, Yasamin MostofiDisentangled Representation Learning Physics-Informed ML
Department of Electrical and Computer Engineering, University of California, Santa Barbara
FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting
Sep 22, 2026Shijie Xiao, Jonathan Lin, Thomas Ehrmann +1Probabilistic Forecasting Physics-Informed ML
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA · School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA · Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA +1
Learning Physics from an Imperfect Ancestor
Sep 21, 2026S. Mohammad Mousavi, Teeratorn Kadeethum, Nikolaos Bouklas +1PDE Surrogate Modeling OOD Generalization
Sibley School of Mechanical and Aerospace Engineering, Cornell University · AI Lab, Siemens Energy · Department of Civil and Systems Engineering, Johns Hopkins University
Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements
Sep 20, 2026Yong-Woon Kim, Jihyeok Lee, Sungtae Park +2OOD Generalization Materials Property Prediction
Department of Computer Engineering, Jeju National University, Jeju 63243, South Korea · Green Hydrogen Global Leading Research Center, Jeju National University, Jeju 63243, South Korea · Faculty of Applied Energy System, Jeju National University, Jeju 63243, South Korea +2
Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System
Sep 17, 2026Melisa Bozaci, Alice CicirelloDynamical Systems System Identification
Department of Engineering, University of Cambridge
Amortizing Physics-Informed Neural Solvers via Graph Hypernetworks
Sep 17, 2026Cheng Jing, Abhishek Verma, Kallol Bera +2Amortized Inference Meta-Learning
Arizona State University · Applied Materials Inc.
Physical knowledge on historical data matters more than enforcing physical constraints on the forecast
Sep 17, 2026Etienne Lehembre, Pascal Audigane, Vincent Nguyen +2Time Series Forecasting Physics-Informed ML
CA, LIFO · Université d’Orléans, INSA CVL, LIFO, UR 4022, Orléans, France · BRGM +3
PhyRestore: Physics-Structured Latent-Factor Restoration
Sep 17, 2026Ahmed Shafee, Chayan LahiriPhysics-Informed ML Noise Robustness
Department of Computer Science Adams State University Alamosa, CO, USA · Department of Geosciences Adams State University Alamosa, CO, USA
Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation
Sep 16, 2026Xi Chen, Jianchuan Yang, Hongde Li +5Coronary Angiography Physics-Informed ML
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. · Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. · Department of Cardiology, Laboratory of Cardiac Structure and Function at Institute of Cardiovascular Diseases, and Cardiac Structure and Function Research Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P.R. China. +2
Fast Learning Rates for Physics-Informed Kernel Methods
Sep 16, 2026Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti +1Kernel Regression Physics-Informed ML
SAIL Unit, Istituto Italiano di Tecnologia, Genoa, Italy · MaLGa Center, DIBRIS, Università degli Studi di Genova, Genoa, Italy
Physics-based prediction, uncertainty quantification and decision-making for IN718 crystallographic texture intensity across LPBF defocus regimes
Sep 16, 2026Yisheng Lu, John Riris, Jie Song +2Materials Property Prediction Physics-Informed ML
Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA · Department of Aerospace & Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA · VT Made, Virginia Tech, Blacksburg, VA 24061, USA +1
Deep learning emergent spacetime from fermionic spectral functions in holography
Sep 16, 2026Koji Hashimoto, Hyun-Sik Jeong, Keun-Young Kim +2High-Energy Physics Physics-Informed ML
Department of Physics, Kyoto University, Kyoto 606-8502, Japan · Asia Pacific Center for Theoretical Physics, Pohang 37673, Korea · Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea +3
Interpretable Patch-Based Deep Learning for Wildfire Spread Prediction from Ensemble Simulations
Sep 16, 2026Marcin Lawenda, Aleksandra Krasicka, David Caballero +2Neural Surrogate Modeling Wildfire Forecasting
Poznan Supercomputing and Networking Center, Poznań, Poland · MeteoGrid, Madrid, Spain · Technical University of Czestochowa, Częstochowa, Poland
Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra
Sep 16, 2026Ziyang Zhang, Qin Li, Vasyl B. Yurchyshyn +4Efficient Neural Network Inference Physics-Informed ML
Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, USA · Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA · Big Bear Solar Observatory, New Jersey Institute of Technology, Big Bear City, CA, USA +1
Lecture notes on Physics Informed Neural Networks, Neural Operators, and their applications
Sep 15, 2026Alessandro BombiniPDE Operator Learning Physics-Informed ML
Istituto Nazionale di Fisica Nucleare, Sezione di Firenze
Automated Physics-Informed Neural-Networks-Based Calibration of Highly Segmented Silicon Telescopes
Sep 15, 2026M. Rejmund, A. Lemasson, P. Morfouace +3Physics-Informed ML
GANIL, CEA/DRF - CNRS/IN2P3, Bd Henri Becquerel, BP 55027, F-14076 Caen Cedex 5, France · CEA, DAM, DIF, F-91297, Arpajon, France · Universit´e Paris-Saclay, CEA, LMCE, F-91680, Bruy`eres-le-Chˆatels, France
Physics-Constrained Digital Twins for Sensor Integrity in Urban Pedestrian Flow: Detecting Stealthy False Data Injection with Conformal Guarantees
Sep 15, 2026Oscar Mogollon Gutierrez, Fatemeh Ghasemi, Mohammadhossein Homaei +2False Data Injection Attacks Physics-Informed ML
Physics Informed Random Feature Neural Networks for Solving PDEs
Sep 14, 2026Chi-An Chen, Chunyang Liao, Ming ZhongPDE Solving Random Feature Methods
Department of Applied Mathematics, Mathematics, Illinois Institute of Technology, Chicago, IL, 60616 · Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR, 72701 · Department of Mathematics, University of Houston, Houston, TX, 77204
Drift Field Net: Learning Ocean Lagrangian advection fields from in-situ and satellite observations
Sep 14, 2026Théo Archambault, Pierre Garcia, Mattia Romero +2Physics-Informed ML Spatiotemporal Forecasting
Amphitrite, Paris, France · Sorbonne Université, LIP6, Pequan, Paris, France · The Ocean Cleanup, Rotterdam, Netherlands +1
Physics Informed Neural Network model for the dynamical study of Abdominal Aortic Aneurysm
Sep 14, 2026Adrián Robles Arques, Martín Ruiz Fernandez, Javier Sanchis +2Neural PDE Solvers Physics-Informed ML
Lucentia Research; Instituto Universitario de Investigación en Informática, Universidad de Alicante, Carr. San Vicent del Raspeig, Alicante, 03690, Comunidad Valenciana, España
QuPAINT: Physics-Aware Multimodal Reasoning for Quantum Material Characterization
Sep 14, 2026Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen +4Multimodal Grounding Synthetic-to-Real Domain Adaptation
Quantum AI Lab in the Department of Electrical Engineering & Computer Science, University of Arkansas, Fayetteville, AR 72701, USA · University of Utah, Salt Lake City, UT, USA · Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
Physics-enriched neural solvers for transient ice-flow simulation
Sep 14, 2026Thomas Gregov, Sebastian Rosier, Brandon Finley +2Neural Surrogate Modeling Neural PDE Solvers
Institute of Earth Surface Dynamics, Universit´e de Lausanne, Lausanne, Switzerland · Department of Geography, Universität Zürich, Zürich, Switzerland
Linearized PINN with pretrained nonlinear layers
Sep 14, 2026Wenhao Chen, Alexandre M. TartakovskyEfficient Neural Network Inference PDE Solving
Civil and Environmental Engineering Department University of Illinois Urbana-Champaign Urbana, IL 61801, USA · Pacific Northwest National Laboratory Richland, WA 99352, USA
Prescreening Point Defects in Semiconductors With Machine Learning
Sep 13, 2026Paul Karlsson, Joel Davidsson, Rickard ArmientoMaterials Property Prediction Physics-Informed ML
Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Sep 13, 2026Sidharth S. Menon, Irina Tezaur, Ameya D. JagtapPDE Solving Gradient Interference
Aerospace Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA. · Sandia National Laboratories, 7011 East Ave, Livermore, CA 94550, USA.
Physics-Informed Neural Networks to Infer the Perpendicular Energy Conductivity in the Scrape-Off Layer of Stellarator Devices
Sep 12, 2026J. Gallego (Departamento de Tecnología, CIEMAT, Spain) +23Physics-Informed ML PDE Inverse Problems
Departamento de Tecnología, CIEMAT, Spain. · Harvard John A. Paulson School of Engineering and Applied Sciences, USA. · Laboratorio Nacional de Fusión, CIEMAT, Spain.

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PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning

Kevin Lee, Alison J. March

NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA

Interpretable Anomaly Detection Convolutional Autoencoder Unsupervised Anomaly Detection Physics-Informed ML Anomaly Detection

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Space weather early warning depends on detecting solar wind transients in in-situ measurements at the first Sun-Earth Lagrange point (L1), before they reach Earth. Fixed thresholds can miss combined magnetic and plasma structure, and many learning methods provide a single anomaly score. We present the Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather (PISCES), a convolutional autoencoder trained without catalog labels on OMNI solar wind measurements under physics constraints. Its loss includes magnetic field consistency, an empirical relation between temperature and velocity, the Parker spiral angle, and penalties on changes between consecutive one-minute samples in derived quantities calculated from the reconstruction. At inference, PISCES separates the anomaly score into magnetic and plasma reconstruction errors, physics relations, and residual corrections, and reports the magnitude of each contribution. Attenuation of the skip connections, selected on validation data, improves average precision for the trained models, while the untrained scores remain nearly the same. The trained models also give a more consistent ordering of these physical contributions. After smoothing with a trailing median, the alarms can precede independently observed sudden commencements, including positive sudden impulses.

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