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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 21–30 of 192 papers

TitleStatusHype
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaicsCode1
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systemsCode1
Physics-informed neural networks for corrosion-fatigue prognosisCode1
Fleet Prognosis with Physics-informed Recurrent Neural NetworksCode1
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Laser Powder Bed Fusion—0
TS-PIELM: Time-Stepping Physics-Informed Extreme Learning Machine Facilitates Soil Consolidation Analyses—0
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations—0
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient—0
Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy—0
A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse DataCode0
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