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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 61–70 of 192 papers

TitleStatusHype
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
An operator preconditioning perspective on training in physics-informed machine learning—0
KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics—0
Learning ergodic averages in chaotic systems—0
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation—0
Calibrating constitutive models with full-field data via physics informed neural networks—0
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis—0
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations—0
Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning—0
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