MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations

11/01/2022
by   Saurabh Deshpande, et al.
0

In many cutting-edge applications, high-fidelity computational models prove too slow to be practical and are thus replaced by much faster surrogate models. Recently, deep learning techniques have become increasingly important in accelerating such predictions. However, they tend to falter when faced with larger and more complex problems. Therefore, this work introduces MAgNET: Multi-channel Aggregation Network, a novel geometric deep learning framework designed to operate on large-dimensional data of arbitrary structure (graph data). MAgNET is built upon the MAg (Multichannel Aggregation) operation, which generalizes the concept of multi-channel local operations in convolutional neural networks to arbitrary non-grid inputs. The MAg layers are interleaved with the proposed novel graph pooling/unpooling operations to form a graph U-Net architecture that is robust and can handle arbitrary complex meshes, efficiently performing supervised learning on large-dimensional graph-structured data. We demonstrate the predictive capabilities of MAgNET for several non-linear finite element simulations and provide open-source datasets and codes to facilitate future research.

READ FULL TEXT

page 19

page 23

page 24

page 27

research
12/01/2022

Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Deep learning surrogate models are being increasingly used in accelerati...
research
11/02/2021

Probabilistic Deep Learning for Real-Time Large Deformation Simulations

For many novel applications, such as patient-specific computer-aided sur...
research
11/18/2022

Point-Cloud-based Deep Learning Models for Finite Element Analysis

In this paper, we explore point-cloud based deep learning models to anal...
research
05/31/2017

3D Mesh Segmentation via Multi-branch 1D Convolutional Neural Networks

3D mesh segmentation is an important research area in computer graphics,...
research
12/06/2022

Graphnics: Combining FEniCS and NetworkX to simulate flow in complex networks

Network models facilitate inexpensive simulations, but require careful h...
research
05/14/2019

Graph Attribute Aggregation Network with Progressive Margin Folding

Graph convolutional neural networks (GCNNs) have been attracting increas...
research
05/08/2023

Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging

Reconstruction of tomographic images from boundary measurements requires...

Please sign up or login with your details

Forgot password? Click here to reset