Data Scoping: Effectively Learning the Evolution of Generic Transport PDEs

• URL: http://arxiv.org/abs/2405.01319v1
• Date: Thu, 2 May 2024 14:24:56 GMT
• Title: Data Scoping: Effectively Learning the Evolution of Generic Transport PDEs
• Authors: Jiangce Chen, Wenzhuo Xu, Zeda Xu, Noelia Grande Gutiérrez, Sneha Prabha Narra, Christopher McComb,
• Abstract summary: Transport PDEs are governed by time-dependent partial differential equations (PDEs) describing mass, momentum, and energy conservation. Deep learning architectures are fundamentally incompatible with the simulation of these PDEs. This paper proposes a distributed data scoping method with linear time complexity to limit the scope of information to predict the local properties.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Transport phenomena (e.g., fluid flows) are governed by time-dependent partial differential equations (PDEs) describing mass, momentum, and energy conservation, and are ubiquitous in many engineering applications. However, deep learning architectures are fundamentally incompatible with the simulation of these PDEs. This paper clearly articulates and then solves this incompatibility. The local-dependency of generic transport PDEs implies that it only involves local information to predict the physical properties at a location in the next time step. However, the deep learning architecture will inevitably increase the scope of information to make such predictions as the number of layers increases, which can cause sluggish convergence and compromise generalizability. This paper aims to solve this problem by proposing a distributed data scoping method with linear time complexity to strictly limit the scope of information to predict the local properties. The numerical experiments over multiple physics show that our data scoping method significantly accelerates training convergence and improves the generalizability of benchmark models on large-scale engineering simulations. Specifically, over the geometries not included in the training data for heat transferring simulation, it can increase the accuracy of Convolutional Neural Networks (CNNs) by 21.7 \% and that of Fourier Neural Operators (FNOs) by 38.5 \% on average.

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