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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 61–70 of 192 papers

TitleStatusHype
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine LearningCode0
Physical Data Embedding for Memory Efficient AI—0
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain HyperelasticityCode0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
Physics-Informed Machine Learning for Smart Additive Manufacturing—0
Predicting 3D Rigid Body Dynamics with Deep Residual Network—0
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator—0
Finite Operator Learning: Bridging Neural Operators and Numerical Methods for Efficient Parametric Solution and Optimization of PDEsCode1
Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning—0
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines—0
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