Multi-Scale Message Passing Neural PDE Solvers
METADATA ONLY
Loading...
Author / Producer
Date
2023-02
Publication Type
Report
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
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.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
2023-14
Pages / Article No.
Publisher
Seminar for Applied Mathematics, ETH Zurich
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03851 - Mishra, Siddhartha / Mishra, Siddhartha
Notes
Funding
Related publications and datasets
Is cited by: