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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 171–180 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
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework—0
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning—0
Further Exploration of Precise Binding Energies from Physics Informed Machine Learning and the Development of a Practical Ensemble Model—0
Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks—0
Grey-box models for wave loading prediction—0
h-analysis and data-parallel physics-informed neural networks—0
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