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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 51–60 of 192 papers

TitleStatusHype
State-space models are accurate and efficient neural operators for dynamical systemsCode0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
(Un)supervised Learning of Maximal Lyapunov Functions—0
Spectrally Informed Learning of Fluid Flows—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise—0
Physics-Informed Machine Learning for Grade Prediction in Froth Flotation—0
Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling—0
Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold NetworksCode2
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
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