A deep learning approach based on the physics-informed neural networks for Gaussian thermal shock-induced thermoelastic wave propagation analysis in a thick hollow cylinder with energy dissipation-Reference-Cited by-同舟云学术

A deep learning approach based on the physics-informed neural networks for Gaussian thermal shock-induced thermoelastic wave propagation analysis in a thick hollow cylinder with energy dissipation

Published:2022-06-07 Issue: Volume: Page:1-40
ISSN:1745-5030
Container-title:Waves in Random and Complex Media
language:en
Short-container-title:Waves in Random and Complex Media

Author:

Eshkofti Katayoun¹,Hosseini Seyed Mahmoud¹^ORCID

Affiliation:

1. Industrial Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Publisher

Informa UK Limited

Subject

General Physics and Astronomy,General Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/17455030.2022.2083264

Reference93 articles.

1. Meshless local Petrov–Galerkin method for coupled thermoelasticity analysis of a functionally graded thick hollow cylinder

2. Shock-induced thermoelastic wave propagation analysisin a thick hollow cylinder without energy dissipation using mesh-free generalized finite difference (GFD) method

3. Application of a hybrid mesh-free method for shock-induced thermoelastic wave propagation analysis in a layered functionally graded thick hollow cylinder with nonlinear grading patterns

4. Thermoelastic diffusion problem for a half-space due to a refined dual-phase-lag Green-Naghdi model

5. Modified DPL Green–Naghdi theory for thermoelastic vibration of temperature-dependent nanobeams

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