Physics-informed learning for thermophysical field reconstruction and parameter measurement in a nano-porous insulator's heat transfer problem-Reference-Cited by-同舟云学术

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Physics-informed learning for thermophysical field reconstruction and parameter measurement in a nano-porous insulator's heat transfer problem

Published:2023-11 Issue: Volume:148 Page:107045
ISSN:0735-1933
Container-title:International Communications in Heat and Mass Transfer
language:en
Short-container-title:International Communications in Heat and Mass Transfer

Author:

Pang Hao-Qiang,Shao Xia,Zhang Zi-Tong,Xie Xin,Dai Ming-Yang,Guo Jiang-Feng,Zhang Yan-Bo,Liu Tian-Yuan,Gao Yan-Feng

Publisher

Elsevier BV

Subject

Condensed Matter Physics,General Chemical Engineering,Atomic and Molecular Physics, and Optics

Reference53 articles.

1. A fast implementation of coalitional model predictive controllers based on machine learning: application to solar power plants [J];Masero;Eng. Appl. Artif. Intell.,2023

2. Temperature field inversion of heat-source systems via physics-informed neural networks [J];Liu;Eng. Appl. Artif. Intell.,2022

3. Thermal neural networks: lumped-parameter thermal modeling with state-space machine learning [J];Kirchgässner;Eng. Appl. Artif. Intell.,2023

4. Joint deep reversible regression model and physics-informed unsupervised learning for temperature field reconstruction [J];Gong;Eng. Appl. Artif. Intell.,2023

5. Coating-boosted interfacial thermal transport for carbon nanotube array nano-thermal interface materials [J];Qiu;Carbon,2019

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2. A parameterized physics-informed machine learning approach for solving heat and mass transfer equations in the drying process;International Communications in Heat and Mass Transfer;2024-11

3. A novel identification method for the non-equilibrium heat transfer problem in porous material;Engineering with Computers;2024-03-03

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