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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 41–50 of 192 papers

TitleStatusHype
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning—0
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations—0
DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training—0
AdjointNet: Constraining machine learning models with physics-based codes—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
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields—0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion—0
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning—0
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