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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Building The Ph(ysical)AI Layer Of Machine Intelligence
Jun 2, 2026Ulbert Jose Botero, Liam Smith, Brooks Olney +5Cross-Modal Learning Cross-Modal Representation Learning
MIT Lincoln Laboratory
APIC: Amortized Physics-Informed Calibration using Neural Processes
Jun 2, 2026Aishwarya Venkataramanan, Sai Karthikeya Vemuri, Joachim DenzlerUncertainty Quantification Amortized Inference
Computer Vision Group, Friedrich Schiller University Jena, Germany
A Geometric Lens on Physics-Aligned Data Compression
Jun 2, 2026Aleix Segui, Wesley ArmourRate-Distortion Theory Rate-Distortion Optimization
Department of Engineering Science, University of Oxford, Oxford, UK.
Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
Jun 2, 2026Yongjin Choi, Hyeonbin Moon, Seunghwa RyuDifferentiable Physics Physics-Informed ML
KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
Physics-Guided Recurrent State-Space Neural Networks for Multi-Step Prediction
Jun 1, 2026Ruiyuan Li, Ajay Seth, Manon KokTime Series Forecasting Recurrent Neural Networks
Delft Center for Systems and Control, TU Delft, the Netherlands · Department of Biomechanical Engineering, TU Delft, the Netherlands
Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations
Jun 1, 2026Deyu Zhuang, Peiliang Gong, Yang Shao +4Irregular Time-Series Modeling Stochastic Differential Equations
Nanjing University of Aeronautics and Astronautics Nanjing, China · Nanyang Technological University Singapore · Singapore University of Technology and Design Singapore
Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition
Jun 1, 2026Chinthaka Ranasingha, Tharindu Fernando, Sridha Sridharan +2Human Activity Recognition Temporal Convolutional Networks
Signal Processing, Artificial Intelligence and Vision Technologies (SAIVT) Research Group, School of Electrical Engineering and Robotics, Queensland University of Technology (QUT), Brisbane, QLD 4000, Australia
Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
Jun 1, 2026Zixuan Jin, Wenzhuo Zhang, Shuxian Quan +4Multi-Robot Systems Swarm Robotics
School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China. · Shunde Innovation School, University of Science and Technology Beijing, 528399, Foshan Guangdong, China.
PINNOCHIO: Physics-Informed Neural Network for Coupled Hyperelastic Interface-Volume Simulation in Orthognathic Surgery
Jun 1, 2026Jungwook Lee, Daeseung Kim, Kevin Gu +6Neural Surrogate Modeling Hyperelasticity
Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA · Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA · Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies
May 31, 2026Biswajeet Sahoo, Debadutta PatraPhysics-Informed ML
Durham University, Durham, UK · Department of Chemical Engineering Veer Surendra Sai University of Technology, Burla, India
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
May 31, 2026Hoang Anh Nguyen, Divakar Vashisth, Ali TuraHybrid Quantum-Classical ML Physics-Informed ML
Department of Geophysics, Colorado School of Mines, Golden, CO 80401 USA · Department of Energy Science and Engineering, Stanford University, Stanford, CA 94305 USA · Department of Petroleum Engineering, Colorado School of Mines, Golden, CO 80401 USA
Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling
May 31, 2026Boris KriukPhysics-Informed ML
The Hong Kong University of Science and Technology, Hong Kong SAR
Data Enrichment for Symbolic Regression Using Diffusion Models
May 31, 2026Simon De Reuver, Tamas Kristof Toth, Teddy LazebnikPhysics-Informed Diffusion Models Physics-Informed Generative Modeling
Department of Computing, Jönköping University, Jönköping, Sweden · Department of Information Science, University of Haifa, Haifa, Israel
Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment
May 30, 2026Uiwon Hwang, Jaeho HwangNeural Network Robustness Physics-Informed ML
Department of Computer Science and Engineering Ewha Womans University · Human-Centered Artificial Intelligence Research Institute Ewha Womans University
Graph Transfer Learning via Shared Latent Geometry: Theory and Applications
May 30, 2026Tong Wu, Andrew Campbell, Anna ScaglioneZero-Shot Learning Transfer Learning
University of Central Florida, USA · Cornell University, USA
Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
May 30, 2026Nathanael Tepakbong, Hanyu Hu, Chengyu Liu +1Neural Network Approximation Theory Physics-Informed ML
Department of Data Science, City University of Hong Kong, 83 Tat Chee Ave, Hong Kong · Department of Mathematics, City University of Hong Kong, 83 Tat Chee Ave, Hong Kong
Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise
May 29, 2026Arpan Biswas, Sutirtha Paul, Joseph Agada +2Surrogate Modeling Uncertainty Quantification
University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Institute for Advanced Materials and Manufacturing, University of Tennessee, Knoxville, Tennessee 37996, USA · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA. +2
Practical Cross-Band Channel Prediction for AI-RAN via Physics-Guided Deep Unfolding
May 29, 2026Ruiqi Kong, He Chen, Xiaojun LinWireless Communications Physics-Informed ML
Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
Oscillatory State-Space Models as Inductive Biases for Physics-Informed Neural PDE Solvers
May 29, 2026Abhishek Chandra, Taniya KapoorNeural PDE Solvers Physics-Informed ML
KTH Royal Institute of Technology Stockholm, Sweden · Wageningen University & Research Wageningen, The Netherlands
Physics-Informed Coarsening for Multigrid Graph Neural Surrogates
May 29, 2026Amir Bazzi, David Cardinaux, Ramy Nemer +3Graph Neural Networks Neural PDE Solvers
1CEMEF, Mines Paris – PSL, Sophia Antipolis, France · 2Aubert et Duval, France · 3Transvalor, Mougins, France.
A physics-informed foundation model for quantitative diffusion MRI
May 29, 2026Zihan Li, Jialan Zheng, Ziyu Li +18Physics-Informed Diffusion Models Medical Imaging Foundation Models
School of Biomedical Engineering, Tsinghua University, Beijing, China. · Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom. · Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China. +4
PINNs Failure Modes are Overfitting
May 29, 2026Nigel T. Andersen, Takashi MatsubaraNeural Network Generalization Neural PDE Solvers
Graduate School of Information Science and Technology Hokkaido University Sapporo, Japan · RIKEN Center for Advanced Intelligence Project (AIP) Tokyo, Japan
Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
May 28, 2026Shengchao Chen, Ting Shu, Sufen RenLLM Agent Skill Learning Physics-Informed ML
AAII, University of Technology Sydney · School of Artificial Intelligence, Shenzhen University · School of Information and Communication Engineering, Hainan University
Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
May 27, 2026Yuxin Wang, Yuanzhe Hu, Xiaokun Zhong +7Neural Network Optimization Scientific ML
Dartmouth College · University of California, San Diego · University of California, Berkeley +3
Faster Thermal Profiling of a Lunar Rover with Machine Learning Adapted Finite Difference Model
May 26, 2026Samuel Weber, Zaki Hasnain, Souma ChowdhuryPDE Solving PDE Surrogate Modeling
University at Buffalo · Department of Mechanical and Aerospace Engineering · Zij Sciences +1
PIDM-DP: Physics-Informed Diffusion with Dormand-Prince Integration for Chaotic System Identification and State Reconstruction across Multiple Dynamical Regimes
May 26, 2026Shailendra DabralPhysics-Informed Diffusion Models Nonlinear System Identification
Indian Institute of Technology Indore
CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout
May 26, 2026Tongxi Song, Ziyu Li, Zihan Li +6Image Reconstruction Accelerated MRI Reconstruction
School of Biomedical Engineering, Tsinghua Medicine, Tsinghua University, Beijing, China · Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK · Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States +1
Deep Learning-based Algebraic Reynolds Stress Closures for RANS Simulations of Turbulent Flows
May 25, 2026Daniel Dehtyriov, Jonathan F. MacArt, Justin SirignanoPhysics-Informed ML Computational Fluid Dynamics
Mathematical Institute University of Oxford Oxford OX2 6GG, United Kingdom · Aerospace and Mechanical Engineering University of Notre Dame Notre Dame, IN 46556, USA
A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
May 25, 2026Thien V. Nguyen, Amaury Habrard, Benjamin GuedjPAC-Bayesian Generalization Bounds Physics-Informed ML
Université Jean Monnet Saint-Étienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Étienne, France · Université Jean Monnet Saint-Étienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, Inria, F-42023, Saint-Étienne, France · Institut Universitaire de France +1
PhyPush: One Push is All You Need for Sensorless Physical Property Estimation with Physics-Guided Transformers
May 25, 2026Koyo Fujii, Luis Figueredo, Praminda Caleb-Solly +4Physical Reasoning Robotic Manipulation
School of Computer Science, University of Nottingham, Nottingham, United Kingdom · Dept. of Industrial Engineering, University of Bologna, Bologna, Italy

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Building The Ph(ysical)AI Layer Of Machine Intelligence

Ulbert Jose Botero, Liam Smith, Brooks Olney, Pooya Khorrami+4

MIT Lincoln Laboratory

Cross-Modal Learning Cross-Modal Representation Learning Physics-Informed ML

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Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data. We propose principle-driven foundation models that encode signal-theoretic principles (Fourier decomposition, energy conservation, symmetry) rather than learn untethered statistical correlations. We hypothesize that domains differ not in fundamental physics, but in learnable transformations in time, frequency, magnitude, or phase. Training exclusively on radio-frequency (RF) data with co-designed architecture and losses incorporating these principles, we achieve cross-modal transfer to audio, images, text, and video using only frozen representations learned from RF data, requiring no fine-tuning of the encoder on target domains. Our 1.99M parameter frozen encoder achieves 77.7% average accuracy (91.9% top-3) across 15 diverse tasks via linear probing, with systematic variation: 84.5 on physically-grounded tasks (speaker recognition, seismology, RF fingerprinting) versus 70.0% on semantic tasks (music genre, language recognition). This reveals that principle-driven and scale-driven approaches offer complementary paths: physical principles enable efficient cross-modal transfer while naturally establishing the boundary between physical and semantic understanding.

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