# Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity

@article{Arora2021MachineLC, title={Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity}, author={Rajat Arora}, journal={ArXiv}, year={2021}, volume={abs/2112.08676} }

This work presents a novel physics-informed deep learning based super-resolution framework to reconstruct high-resolution deformation fields from low-resolution counterparts, obtained from coarse mesh simulations or experiments. We leverage the governing equations and boundary conditions of the physical system to train the model without using any high-resolution labeled data. The proposed approach is applied to obtain the super-resolved deformation fields from the low-resolution stress and…

## 3 Citations

### PhySRNet: Physics informed super-resolution network for application in computational solid mechanics

- Computer Science
- 2022

The proposed framework provides possibilities for guiding future subgrid-scale models for modeling complex phenomena occurring at small spatial and temporal scales and opens the door to applying machine learning and traditional numerical approaches in tandem to reduce computational complexity accelerate scientiﬁc discovery and engineering design.

### Physics-informed neural networks for modeling rate- and temperature-dependent plasticity

- Materials ScienceArXiv
- 2022

A physics-informed neural network based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids and a fundamental challenge involving selection of appropriate model outputs so that the mechanical problem can be faithfully solved using neural networks is highlighted.

### A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

- Computer ScienceComputer Methods in Applied Mechanics and Engineering
- 2022

By properly designing the network architecture in PINN, the deep learning model has the potential to solve the unknowns in a heterogeneous domain without any available initial data from other sources.

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