Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks

Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks

Optimizing heat conduction layout is essential during engineering design, especially for sensible thermal products. However, when the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods usually lead to a substantial computational cost. T...

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Bibliographic Details
Main Authors: Yang Sun, Abdussalam Elhanashi, Hao Ma, Mario Rosario Chiarelli
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Applied Sciences
Subjects:
steady heat conduction
layout optimization
deep learning
convolutional neural networks
physics-driven method
particle swarm optimization
Online Access:https://www.mdpi.com/2076-3417/12/21/10986

Internet

https://www.mdpi.com/2076-3417/12/21/10986

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