PhySRNet: Physics informed super-resolution network for application in computational solid mechanics
Author:
Affiliation:
1. Advanced Micro Devices Inc. (AMD),Austin,USA
Funder
Carnegie Mellon University (CMU)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10027510/10027513/10027589.pdf?arnumber=10027589
Reference44 articles.
1. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;raissi;Journal of Computational Physics,2019
2. Machine learning-accelerated computational solid mechanics: Application to linear elasticity;arora;ArXiv Preprint,2021
3. Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations;raissi;ArXiv Preprint,2017
4. Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
5. Physics-informed deep learning for incompressible laminar flows
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1. Physics-Informed Computer Vision: A Review and Perspectives;ACM Computing Surveys;2024-08-20
2. AttGAN: attention gated generative adversarial network for spatio-temporal super-resolution of ocean phenomena;International Journal of Digital Earth;2024-07-08
3. A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems;Multiscale Science and Engineering;2024-02-13
4. Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics;Journal of Computing and Information Science in Engineering;2024-01-29
5. Fast and Accurate Simulation of Deformable Solid Dynamics on Coarse Meshes;2024
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