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This work presents a physics-informed deep learning-based super-resolution framework to enhance the spatio-temporal resolution of the solution of time-dependent partial differential equations (PDE).
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Martin Alnæs, Jan Blechta, Johan Hake, August Johansson, Benjamin Kehlet, Anders Logg, Chris Richardson, Johannes Ring, Marie E Rognes, and Garth N Wells · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
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Computational Approximation of Mesoscale Field Dislocation Mechanics at Finite Deformation
Rajat Arora · 2019
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Dislocation pattern formation in finite deformation crystal plasticity
Rajat Arora and Amit Acharya · 2020
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A unification of finite deformation J2 Von-Mises plasticity and quantitative dislocation mechanics
Rajat Arora and Amit Acharya · 2020
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Finite element approximation of finite deformation dislocation mechanics
Rajat Arora, Xiaohan Zhang, and Amit Acharya · 2020
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Meshfreeflownet: a physics-constrained deep continuous space-time super-resolution framework
Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A Tchelepi, Philip Marcus, Mr Prabhat, Anima Anandkumar, et al · 2020
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Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model
Ari Frankel, Kousuke Tachida, and Reese Jones · 2020
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Phygeonet: Physics-informed geometry-adaptive convolutional neural networks for solving parametric pdes on irregular domain
Han Gao, Luning Sun, and Jian-Xun Wang · 2020
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Equilibrium shape of misfitting precipitates with anisotropic elasticity and anisotropic interfacial energy
Tushar Joshi, Rajat Arora, Anup Basak, and Anurag Gupta · 2020
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Pytorch distributed: Experiences on accelerating data parallel training
Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, et al · 2020
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Physics informed deep learning for computational elastodynamics without labeled data
Chengping Rao, Hao Sun, and Yang Liu · 2020
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Luning Sun, Han Gao, Shaowu Pan, and Jian-Xun Wang · 2020
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Machine learning-accelerated computational solid mechanics: Application to linear elasticity
Mechanics of micropillar confined thin film plasticity
Abhishek Arora, Rajat Arora, and Amit Acharya · 2022
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Physics-informed neural networks for modeling rate-and temperature-dependent plasticity
Rajat Arora, Pratik Kakkar, Biswadip Dey, and Amit Chakraborty · 2022
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https://github.com/sairajat/superresolutiondynamics
Rajat Arora · 2022
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PhySRNet: Physics informed super-resolution network for application in computational solid mechanics
Rajat Arora · 2022
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Scientific machine learning through physics-informed neural networks: Where we are and what’s next
Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maizar Raissi, and Francesco Piccialli · 2022
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Rajat Arora · 2021
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Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Kai Fukami, Koji Fukagata, and Kunihiko Taira · 2021
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao, Luning Sun, and Jian-Xun Wang · 2021
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Nsfnets (navier-stokes flow nets): Physics-informed neural networks for the incompressible navier-stokes equations
Xiaowei Jin, Shengze Cai, Hui Li, and George Em Karniadakis · 2021
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The old and the new: Can physics-informed deep-learning replace traditional linear solvers?
Stefano Markidis · 2021
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Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
Qiming Zhu, Zeliang Liu, and Jinhui Yan · 2021
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Rethinking the importance of sampling in physics-informed neural networks
Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, and Anuj Karpatne · 2022
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Physics-informed deep super-resolution for spatiotemporal data
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Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning
Ankit Shrivastava, Jingxiao Liu, Kaushik Dayal, and Hae Young Noh · 2022
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Respecting causality is all you need for training physics-informed neural networks
Sifan Wang, Shyam Sankaran, and Paris Perdikaris · 2022
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