Differential Operator Approximation Based Tightly Coupled Multiphysics Solver Using Cascaded Fourier Network,Advanced Theory and Simulations

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Differential Operator Approximation Based Tightly Coupled Multiphysics Solver Using Cascaded Fourier Network
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-08-04 , DOI: 10.1002/adts.202200409
Yinpeng Wang ₁ , Hongyu Gao ₁ , Qiang Ren ₁

Affiliation

Calculating the unknown coupled multiphysics fields from the known boundary condition is of great significance in computational physics. Existing classical algorithms are usually time consuming and resource demanding. Explosive growth in deep learning (DL) has provided a substitutive way to accelerate the calculation process by fully making use of the parallel computing capability of the graphics processing unit. In this work, a cascaded DL framework is presented to compute the coupled multiphysics fields related to electrical potential, temperature, velocity, etc. The whole framework is comprised of an input module and several Fourier modules, which achieves to acquire the coupled fields from the explicit boundary conditions. Compared with conventional networks, the Fourier network emerges consistent average error among different resolutions which saves training costs. As a result, a fully trained network can obtain the expected physics fields in real time, offering rosy prospects for practical scenarios.

中文翻译：

基于微分算子逼近的紧耦合多物理场求解器使用级联傅里叶网络

从已知边界条件计算未知耦合多物理场在计算物理中具有重要意义。现有的经典算法通常非常耗时且需要大量资源。深度学习（DL）的爆发式增长，充分利用图形处理单元的并行计算能力，提供了一种加速计算过程的替代方式。在这项工作中，提出了一个级联 DL 框架来计算与电势、温度、速度等相关的耦合多物理场。整个框架由一个输入模块和几个傅里叶模块组成，实现了从明确的边界条件。与传统网络相比，傅里叶网络在不同分辨率之间出现一致的平均误差，从而节省了训练成本。因此，一个经过充分训练的网络可以实时获得预期的物理场，为实际场景提供了美好的前景。

更新日期：2022-08-04

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