mlsanityData to 14 Oct 2026

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

arXiv 2206.15457 ↗First posted 30 Jun 2022cond-mat.mtrl-sci cs.LGcited by 7

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Abstract (arXiv)

Traditional approaches based on finite element analyses have been successfully used to predict the macro-scale behavior of heterogeneous materials (composites, multicomponent alloys, and polycrystals) widely used in industrial applications. However, this necessitates the mesh size to be smaller than the characteristic length scale of the microstructural heterogeneities in the material leading to computationally expensive and time-consuming calculations. The recent advances in deep learning based image super-resolution (SR) algorithms open up a promising avenue to tackle this computational challenge by enabling researchers to enhance the spatio-temporal resolution of data obtained from coarse mesh simulations. However, technical challenges still remain in developing a high-fidelity SR model for application to computational solid mechanics, especially for materials undergoing large deformation. This work aims at developing a physics-informed deep learning based super-resolution framework (PhySRNet) which enables reconstruction of high-resolution deformation fields (displacement and stress) from their low-resolution counterparts without requiring high-resolution labeled data. We design a synthetic case study to illustrate the effectiveness of the proposed framework and demonstrate that the super-resolved fields match the accuracy of an advanced numerical solver running at 400 times the coarse mesh resolution while simultaneously satisfying the (highly nonlinear) governing laws. The approach opens the door to applying machine learning and traditional numerical approaches in tandem to reduce computational complexity accelerate scientific discovery and engineering design.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 53 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

20 of 53 references.

References · 20

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  10. 2020MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Frameworkcited by 78
  11. 2019Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networkscited by 76
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8 more
  1. 2020Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networkscited by 42
  2. 2020Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imagingcited by 40
  3. 2020Turbulence Enrichment using Physics-informed Generative Adversarial Networkscited by 18
  4. 2022Physics-informed neural networks for modeling rate- and temperature-dependent plasticitycited by 11
  5. 2019A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relationscited by 9
  6. 2019Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flowscited by 8
  7. 2021Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticitycited by 6
  8. 2022Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder-Decoder Learningcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 1.

7 papers.

  1. 2023Physics-Informed Computer Vision: A Review and Perspectivescited by 28
  2. 2022Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computingcited by 24
  3. 2022Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learningcited by 3
  4. 2023Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulationscited by 1
  5. 2023A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part Icited by 0
  6. 2023An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulationscited by 0
  7. 2024Downscaling Neural Network for Coastal Simulationscited by 0