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

01/31/2020
by   Houpu Yao, et al.
4

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.

READ FULL TEXT

page 11

page 16

page 18

page 19

page 21

page 23

page 24

research
09/17/2019

Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling

Seismic events, among many other natural hazards, reduce due functionali...
research
09/19/2022

Physics-Constrained Neural Network for the Analysis and Feature-Based Optimization of Woven Composites

Woven composites are produced by interlacing warp and weft fibers in a p...
research
01/04/2022

Multi-physics inverse homogenization for the design of innovative cellular materials: application to thermo-mechanical problems

We present a new algorithm to design lightweight cellular materials with...
research
03/18/2022

Constitutive model characterization and discovery using physics-informed deep learning

Classically, the mechanical response of materials is described through c...
research
01/11/2023

Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning

Finite element methods (FEM) are popular approaches for simulation of so...
research
04/01/2022

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements

We present a data-driven workflow to biological tissue modeling, which a...

Please sign up or login with your details

Forgot password? Click here to reset