论文标题
卷积，聚集和基于注意力的深度神经网络，用于加速力学模拟
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
论文作者
论文摘要
深度学习替代模型越来越多地用于加速科学模拟，以替代昂贵的传统数值技术。但是，在处理现实世界中的复杂示例时，它们的使用仍然是一个重大挑战。在这项工作中，我们演示了三种类型的神经网络体系结构，以有效学习固体体型的高度非线性变形。前两个体系结构基于最近提出的CNN U-NET和磁铁（Graph U-NET）框架，这些框架显示出在基于网格的数据上学习的有希望的性能。第三个体系结构是感知者IO，这是一种属于基于注意力的神经网络家族的最新体系结构，该类别彻底改变了各种工程领域，并且在计算机制中仍未探索。我们研究和比较了两个基准示例上所有三个网络的性能，并显示它们能够准确预测软体的非线性机械响应的能力。
Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks--a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
