A gradient-enhanced physics-informed neural network (gPINN) scheme for the coupled non-fickian/non-fourierian diffusion-thermoelasticity analysis: A novel gPINN structure-Reference-Cited by-同舟云学术

A gradient-enhanced physics-informed neural network (gPINN) scheme for the coupled non-fickian/non-fourierian diffusion-thermoelasticity analysis: A novel gPINN structure

Published:2023-11 Issue: Volume:126 Page:106908
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Eshkofti Katayoun,Hosseini Seyed Mahmoud

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

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3. A deep learning method for solving thermoelastic coupling problem;Zeitschrift für Naturforschung A;2024-05-15

4. Simulation of thermal-fluid coupling in silicon single crystal growth based on gradient normalized physics-informed neural network;Physics of Fluids;2024-05-01

5. A new modified deep learning technique based on physics-informed neural networks (PINNs) for the shock-induced coupled thermoelasticity analysis in a porous material;Journal of Thermal Stresses;2024-04-10