Purpose

This study aims to use deep neural networks (DNNs) to learn the conduction heat transfer physics and estimate temperature distribution images in a physical domain without using any physical model or mathematical governing equation.

Design/methodology/approach

Two novel DNNs capable of learning the conduction heat transfer physics were defined. The first DNN (U-Net autoencoder residual network [UARN]) was designed to extract local and global features simultaneously. In the second DNN, a conditional generative adversarial network (CGAN) was used to enhance the accuracy of UARN, which is referred to as CGUARN. Then, novel loss functions, introduced based on outlier errors, were used to train the DNNs.

Findings

A UARN neural network could learn the physics of heat transfer. Within a few epochs, it reached mean and outlier errors that other DNNs could never reach after many epochs. The composite outlier-mean error as a loss function showed excellent performance in training DNNs for physical images. A UARN could excellently capture local and global features of conduction heat transfer, whereas the composite error could accurately guide DNN to extract high-level information by estimating temperature distribution images.

Originality/value

This study offers a unique approach to estimating physical information, moving from traditional mathematical and physical models to machine learning approaches. Developing novel DNNs and loss functions has shown promising results, opening up new avenues in heat transfer physics and potentially other fields.

中文翻译：

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使用深度神经网络估计传导传热的人工智能方法

 目的

本研究旨在使用深度神经网络（DNN）来学习传导传热物理并估计物理域中的温度分布图像，而不使用任何物理模型或数学控制方程。

设计/方法论/途径

定义了两种能够学习传导传热物理的新型 DNN。第一个 DNN（U-Net 自动编码器残差网络 [UARN]）旨在同时提取局部和全局特征。在第二个DNN中，使用条件生成对抗网络（CGAN）来增强UARN的准确性，简称CGUARN。然后，使用基于异常值误差引入的新颖损失函数来训练 DNN。

 发现

UARN 神经网络可以学习传热物理学。在几个 epoch 内，它达到了其他 DNN 在许多 epoch 后永远无法达到的平均误差和异常值误差。作为损失函数的复合异常值平均误差在训练物理图像的 DNN 时表现出了出色的性能。 UARN 可以很好地捕获传导传热的局部和全局特征，而复合误差可以准确指导 DNN 通过估计温度分布图像来提取高级信息。

 原创性/价值

这项研究提供了一种独特的方法来估计物理信息，从传统的数学和物理模型转向机器学习方法。开发新型 DNN 和损失函数已显示出有希望的结果，为传热物理和其他潜在领域开辟了新途径。