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Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, solid mechanics, materials science, etc.
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Modeling, analysis and physics informed neural network approaches for studying the dynamics of covid-19 involving human-human and human-pathogen interaction
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Data-driven approaches for predicting spread of infectious diseases through dinns: Disease informed neural networks
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nn-pinns: Non-newtonian physics-informed neural networks for complex fluid modeling
M. Mahmoudabadbozchelou, G. E. Karniadakis, and S. Jamali · 2022
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S. Thakur, M. Raissi, and A. M. Ardekani · 2022
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Can-pinn: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
P. Chiu, J. C. Wong, C. Ooi, M. H. Dao, and Y. Ong · 2022
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Modalpinn: an extension of physics-informed neural networks with enforced truncated fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors
G. Raynaud, S. Houde, and F. P. Gosselin · 2022
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Geometry aware physics informed neural network surrogate for solving navier-stokes equation (gapinn)
J. Oldenburg, F. Borowski, A. Öner, K. Schmitz, and M. Stiehm · 2022
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Spline-pinn: Approaching pdes without data using fast, physics-informed hermite-spline cnns
N. Wandel, M. Weinmann, M. Neidlin, and R. Klein · 2022
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Physics-informed neural networks for solving reynolds-averaged navier–stokes equations
H. Eivazi, M. Tahani, P. Schlatter, and R. Vinuesa · 2022
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Gw-pinn: A deep learning algorithm for solving groundwater flow equations
X. Zhang, Y. Zhu, J. Wang, L. Ju, Y. Qian, M. Ye, and J. Yang · 2022
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Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks
M. Aliakbari, M. Mahmoudi, P. Vadasz, and A. Arzani · 2022
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Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations
A. D. Jagtap, D. Mitsotakis, and G. E. Karniadakis · 2022
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Physics-informed pointnet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
Ali Kashefi and Tapan Mukerji · 2022
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Physics informed neural networks for continuum micromechanics
A. Henkes, H. Wessels, and R. Mahnken · 2022
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A deep learning energy method for hyperelasticity and viscoelasticity
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A-pinn: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
L. Yuan, Y. Ni, X. Deng, and S. Hao · 2022
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Physrnet: Physics informed super-resolution network for application in computational solid mechanics
R. Arora · 2022
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A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures
C. A. Yan, R. Vescovini, and L. Dozio · 2022
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S. Rezaei, A. Harandi, A. Moeineddin, B. Xu, and S. Reese · 2022
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Physics-informed neural networks for shell structures
J. Bastek and D. M. Kochmann · 2022
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Physics-informed machine learning model for computational fracture of quasi-brittle materials without labelled data
B. Zheng, T. Li, H. Qi, L. Gao, X. Liu, and L. Yuan · 2022
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Physics-informed neural networks for modeling rate-and temperature-dependent plasticity
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An introduction to programming physics-informed neural network-based computational solid mechanics
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Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs
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Multi-scale physical representations for approximating pde solutions with graph neural operators
Leon Migus, Yuan Yin, Jocelyn Ahmed Mazari, and Patrick Gallinari · 2022
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Learning two-phase microstructure evolution using neural operators and autoencoder architectures
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A physics-informed variational deeponet for predicting crack path in quasi-brittle materials
S. Goswami, M. Yin, Y. Yu, and G. E. Karniadakis · 2022
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Towards large-scale learned solvers for parametric pdes with model-parallel fourier neural operators
T. J. Grady, R. Khan, M. Louboutin, Z. Yin, P. A. Witte, R. Chandra, R. J. Hewett, and F. J. Herrmann · 2022
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U-fno—an enhanced fourier neural operator-based deep-learning model for multiphase flow
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A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
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