High-Fidelity Reconstruction of 3D Temperature Fields Using Attention-Augmented CNN Autoencoders With Optimized Latent Space

High-Fidelity Reconstruction of 3D Temperature Fields Using Attention-Augmented CNN Autoencoders With Optimized Latent Space

Understanding and accurately predicting complex three-dimensional (3D) temperature distributions are critical in diverse domains, including climate science and industrial process optimization. This study presents a sophisticated framework employing a convolutional neural network (CNN)-based autoenco...

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Bibliographic Details
Main Authors: Md Fokrul Islam Khan, Zakir Hossain, Arif Hossen, Md Nuho Ul Alam, Abdul Kadar Muhammad Masum, Md Ziay Uddin
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Autoencoder
computational fluid dynamics
fuel plant
singular value decomposition
temperature distribution
Online Access:https://ieeexplore.ieee.org/document/10781395/
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https://ieeexplore.ieee.org/document/10781395/

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