FEA-Net: A physics-guided data-driven model for efficient mechanical response prediction

Houpu Yao, Yi Gao, Yongming Liu

Research output: Contribution to journalArticle

Abstract

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 predict the mechanical response under external loading. Thus, the identification of 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.

Original languageEnglish (US)
Article number112892
JournalComputer Methods in Applied Mechanics and Engineering
Volume363
DOIs
StatePublished - May 1 2020

Keywords

  • Convolutional neural networks
  • Data-driven model
  • Finite Element Analysis
  • Physics-guided learning

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Physics and Astronomy(all)
  • Computer Science Applications

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