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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 81–90 of 192 papers

TitleStatusHype
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks—0
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning—0
Physical Data Embedding for Memory Efficient AI—0
Physics-Informed Machine Learning for Smart Additive Manufacturing—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
Predicting 3D Rigid Body Dynamics with Deep Residual Network—0
Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator—0
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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