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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 161–170 of 192 papers

TitleStatusHype
Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations—0
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
Differentiable Predictive Control for Large-Scale Urban Road Networks—0
Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning—0
Discovering nonlinear resonances through physics-informed machine learning—0
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
Empirical modeling and hybrid machine learning framework for nucleate pool boiling on microchannel structured surfaces—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
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