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Physics-Informed Neural Networks promise to revolutionize science and engineering practice, by introducing domain-aware deep machine learning models into scientific computation.
Ground state structures in ordered binary alloys with second neighbor interactions
Samuel Miller Allen and John W Cahn · 1972
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Neural-network-based approximations for solving partial differential equations
MWMG Dissanayake and N Phan-Thien · 1994
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Artificial neural networks for solving ordinary and partial differential equations
Isaac E Lagaris, Aristidis Likas, and Dimitrios I Fotiadis · 1998
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Understanding and mitigating gradient pathologies in physics-informed neural networks
Sifan Wang, Yujun Teng, and Paris Perdikaris · 2001
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When and why pinns fail to train: A neural tangent kernel perspective
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
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Differentialequations.jl – a performant and feature-rich ecosystem for solving differential equations in julia
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Nvidia simnetˆ { \{ TM } \} : an ai-accelerated multi-physics simulation framework
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Self-adaptive physics-informed neural networks using a soft attention mechanism
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Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence, 2 2019
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Lu Lu, Pengzhan Jin, and George Em Karniadakis · 2019
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Sciann: A keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
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