论文标题
磁铁：基于网格的模拟的图形U-NET体系结构
MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations
论文作者
论文摘要
在许多尖端应用中，高保真计算模型被证明太慢而无法实用，因此被更快的替代模型所取代。最近，越来越多地利用深度学习技术来加速此类预测。为了学习大维和复杂数据，已经开发了特定的神经网络架构，包括卷积和图神经网络。在这项工作中，我们提出了一个新颖的编码器几何深度学习框架，称为Magnet，该框架扩展了众所周知的卷积神经网络，以适应任意的图形结构数据。磁铁由创新的多通道聚合（MAG）层和图形池/不明化层组成，形成了类似于卷积U-NET的图形U-NET体系结构。我们证明了固体力学中非线性有限元模拟的替代模型中磁体的预测能力。
In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, deep learning techniques have increasingly been utilized to accelerate such predictions. To enable learning on large-dimensional and complex data, specific neural network architectures have been developed, including convolutional and graph neural networks. In this work, we present a novel encoder-decoder geometric deep learning framework called MAgNET, which extends the well-known convolutional neural networks to accommodate arbitrary graph-structured data. MAgNET consists of innovative Multichannel Aggregation (MAg) layers and graph pooling/unpooling layers, forming a graph U-Net architecture that is analogous to convolutional U-Nets. We demonstrate the predictive capabilities of MAgNET in surrogate modeling for non-linear finite element simulations in the mechanics of solids.
