Predicting surface heat flux on complex systems via Conv-LSTM

Predicting surface heat flux on complex systems via Conv-LSTM

Existing algorithms with iterations as the principle for 3D inverse heat conduction problems (IHCPs) are usually time-consuming and resource-demanding. With the recent advancements in deep learning techniques, it is possible to apply the neural network to compute IHCPs. In this paper, a new framewor...

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Bibliographic Details
Main Authors: Ren, Q. (Author), Wang, N. (Author), Wang, Y. (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
3d inverse heat conduction problems
Complex networks
Conv-LSTM
Convolutional neural networks
Forecasting
Heat conduction
Heat flux
Inverse heat conduction model
Inverse heat conduction problem
Inverse problems
Inversion
Learning techniques
Long short-term memory
Measured temperatures
Neural-networks
Surface heat fluxes
Transient heat
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