Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
Paper • 2211.08064 • Published
PIML/TIML papers
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An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
Paper • 2607.15077 • Published • 1 -
Fourier Neural Operator for Parametric Partial Differential Equations
Paper • 2010.08895 • Published -
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning
Paper • 2506.17345 • Published -
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Paper • 2403.09110 • Published • 1 -
A Physics-Informed Fourier-Wavelet Transformer for Multiscale Computational Fluid Dynamics Surrogate Modeling
Paper • 2606.24696 • Published • 1 -
Physics Informed Viscous Value Representations
Paper • 2602.23280 • Published • 1 -
Physics-informed neural operator for predictive parametric phase-field modelling
Paper • 2603.09693 • Published -
SPIKE: Sparse Koopman Regularization for Physics-Informed Neural Networks
Paper • 2601.10282 • Published -
Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations
Paper • 2602.01176 • Published -
Toward a Better Understanding of Fourier Neural Operators: Analysis and Improvement from a Spectral Perspective
Paper • 2404.07200 • Published • 2 -
Training Deep Surrogate Models with Large Scale Online Learning
Paper • 2306.16133 • Published