论文标题
FEA-NET：一种物理引导的数据驱动模型，用于有效的机械响应预测
FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction
论文作者
论文摘要
本文提出了一种用于预测材料和结构机械响应的创新物理学指导学习算法。拟议研究的关键概念基于以下事实：物理模型受部分微分方程（PDE）的控制，并且可以使用有限元分析（FEA）来解决其负载/响应映射。基于此，提出了一种特殊类型的深卷积神经网络（DCNN），它利用我们在物理学上的先验知识来构建具有物理意义的架构的数据驱动模型。这种类型的网络称为FEA-NET，用于在外部加载下解决机械响应。因此，机械系统参数及其响应的计算分别被视为FEA-NET的学习和推断。多物理学（例如，机械 - 热分析）和多相问题（例如，具有随机微结构的复合材料）的案例研究用于证明和验证所提出方法的理论和计算优势。
An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Differential Equation (PDE), and its loading/ response mapping can be solved using Finite Element Analysis (FEA). Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning. This type of network is named as FEA-Net and is used to solve the mechanical response under external loading. Thus, the identification of a mechanical system parameters and the computation of its responses are treated as the learning and inference of FEA-Net, respectively. Case studies on multi-physics (e.g., coupled mechanical-thermal analysis) and multi-phase problems (e.g., composite materials with random micro-structures) are used to demonstrate and verify the theoretical and computational advantages of the proposed method.
