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

• URL: http://arxiv.org/abs/2002.01893v1
• Date: Fri, 31 Jan 2020 09:37:44 GMT
• Title: FEA-Net: A Physics-guided Data-driven Model for Efficient Mechanical Response Prediction
• Authors: Houpu Yao, Yi Gao, Yongming Liu
• Abstract summary: An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. 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.
• Score: 20.661387229686312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Solving Differential Equations using Physics-Informed Deep Equilibrium Models [4.237218036051422]
  This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs) By bridging deep learning and physics-based modeling, this work advances computational techniques for solving IVPs, with implications for scientific computing and engineering applications.
  arXiv  Detail & Related papers  (2024-06-05T17:25:29Z)
• Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning [18.1180892910779]
  Physics-informed neural networks (PINNs) have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations. We provide a unified framework in which analysis of the various components of the error incurred by PINNs in approximating PDEs can be effectively carried out.
  arXiv  Detail & Related papers  (2024-01-30T10:43:27Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Physics Informed Recurrent Neural Networks for Seismic Response Evaluation of Nonlinear Systems [0.0]
  This paper proposes a novel approach for evaluating the dynamic response of multi-degree-of-freedom (MDOF) systems. The focus of this paper is to evaluate the seismic (earthquake) response of nonlinear structures. The predicted response will be compared to state-of-the-art methods such as FEA to assess the efficacy of the physics-informed RNN model.
  arXiv  Detail & Related papers  (2023-08-16T20:06:41Z)
• Symmetry Group Equivariant Architectures for Physics [52.784926970374556]
  In the domain of machine learning, an awareness of symmetries has driven impressive performance breakthroughs. We argue that both the physics community and the broader machine learning community have much to understand.
  arXiv  Detail & Related papers  (2022-03-11T18:27:04Z)
• Physically Explainable CNN for SAR Image Classification [59.63879146724284]
  In this paper, we propose a novel physics guided and injected neural network for SAR image classification. The proposed framework comprises three parts: (1) generating physics guided signals using existing explainable models, (2) learning physics-aware features with physics guided network, and (3) injecting the physics-aware features adaptively to the conventional classification deep learning model for prediction. The experimental results show that our proposed method substantially improve the classification performance compared with the counterpart data-driven CNN.
  arXiv  Detail & Related papers  (2021-10-27T03:30:18Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Hessian-based toolbox for reliable and interpretable machine learning in physics [58.720142291102135]
  We present a toolbox for interpretability and reliability, extrapolation of the model architecture. It provides a notion of the influence of the input data on the prediction at a given test point, an estimation of the uncertainty of the model predictions, and an agnostic score for the model predictions. Our work opens the road to the systematic use of interpretability and reliability methods in ML applied to physics and, more generally, science.
  arXiv  Detail & Related papers  (2021-08-04T16:32:59Z)
• Encoding physics to learn reaction-diffusion processes [18.187800601192787]
  We show how a deep learning framework that encodes given physics structure can be applied to a variety of problems regarding the PDE system regimes. The resultant learning paradigm that encodes physics shows high accuracy, robustness, interpretability and generalizability demonstrated via extensive numerical experiments.
  arXiv  Detail & Related papers  (2021-06-09T03:02:20Z)
• Hard Encoding of Physics for Learning Spatiotemporal Dynamics [8.546520029145853]
  We propose a deep learning architecture that forcibly encodes known physics knowledge to facilitate learning in a data-driven manner. The coercive encoding mechanism of physics, which is fundamentally different from the penalty-based physics-informed learning, ensures the network to rigorously obey given physics.
  arXiv  Detail & Related papers  (2021-05-02T21:40:39Z)
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features [42.96944345045462]
  textscOrbNet is shown to outperform existing methods in terms of learning efficiency and transferability. For applications to datasets of drug-like molecules, textscOrbNet predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.
  arXiv  Detail & Related papers  (2020-07-15T22:38:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.