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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 91–100 of 192 papers

TitleStatusHype
A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential EquationsCode1
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning—0
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems—0
Value Approximation for Two-Player General-Sum Differential Games with State ConstraintsCode0
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks—0
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics—0
A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements—0
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework—0
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator LearningCode0
Overview of Physics-Informed Machine Learning Inversion of Geophysical Data—0
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