FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction

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 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.

References in corpus (13)

• Adam: A Method for Stochastic Optimization
• TensorFlow: A system for large-scale machine learning
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Fully Convolutional Networks for Semantic Segmentation
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
• MeshCNN: A Network with an Edge
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
• Accelerating Eulerian Fluid Simulation With Convolutional Networks
• PDE-Net: Learning PDEs from Data
• Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
• tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
• Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors
• Learning Neural PDE Solvers with Convergence Guarantees

Cited by in corpus (10)

• Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
• Data-Driven Design for Metamaterials and Multiscale Systems: A Review
• Deep Learning in Deterministic Computational Mechanics
• Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
• On the use of graph neural networks and shape-function-based gradient computation in the deep energy method
• Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
• Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
• Explainable artificial intelligence for mechanics: physics-informing neural networks for constitutive models
• How machine learning can help the design and analysis of composite materials and structures?
• A Memory Efficient Adjoint Method to Enable Billion Parameter Optimization on a Single GPU in Dynamic Problems