Multi-Scale Message Passing Neural PDE Solvers

02/07/2023
by   Léonard Equer, et al.
0

We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time-dependent PDEs. Our algorithm possesses both temporal and spatial multi-scale resolution features by incorporating multi-scale sequence models and graph gating modules in the encoder and processor, respectively. Benchmark numerical experiments are presented to demonstrate that the proposed algorithm outperforms baselines, particularly on a PDE with a range of spatial and temporal scales.

READ FULL TEXT

page 9

page 10

research
04/15/2022

Learning time-dependent PDE solver using Message Passing Graph Neural Networks

One of the main challenges in solving time-dependent partial differentia...
research
10/25/2019

Multi-scale Deep Neural Networks for Solving High Dimensional PDEs

In this paper, we propose the idea of radial scaling in frequency domain...
research
02/21/2023

AttentionMixer: An Accurate and Interpretable Framework for Process Monitoring

An accurate and explainable automatic monitoring system is critical for ...
research
08/17/2023

Half-Hop: A graph upsampling approach for slowing down message passing

Message passing neural networks have shown a lot of success on graph-str...
research
06/09/2021

Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks

Continuum mechanics simulators, numerically solving one or more partial ...
research
09/10/2019

Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection

Recent saliency models extensively explore to incorporate multi-scale co...
research
12/19/2019

Temporal Normalizing Flows

Analyzing and interpreting time-dependent stochastic data requires accur...

Please sign up or login with your details

Forgot password? Click here to reset