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Integrating physical inductive biases into machine learning can improve model generalizability.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations, 2017
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Physics informed deep learning (part ii): Data-driven discovery of nonlinear partial differential equations, 2017
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Clebsch-gordan nets: a fully fourier space spherical convolutional neural network
Risi Kondor, Zhen Lin, and Shubhendu Trivedi · 2018
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2019
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Neural ordinary differential equations, 2019
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2019
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Learning neural pde solvers with convergence guarantees, 2019
Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, and Stefano Ermon · 2019
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Maziar Raissi, Alireza Yazdani, and George Em Karniadakis · 2020
Cited alongside, same era.
Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2020
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian B Fuchs, Daniel E Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Physics-informed machine learning
George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Machine-learning non-conservative dynamics for new-physics detection, 2021
Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, and Tie-Yan Liu · 2021
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Machine-learning hidden symmetries, 2021
Ziming Liu and Max Tegmark · 2021
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E (n) equivariant graph neural networks
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Neural sdes as infinite-dimensional gans, 2021
Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, and Terry Lyons · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Ai feynman: A physics-inspired method for symbolic regression
Silviu-Marian Udrescu and Max Tegmark · 2020
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Ai feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark · 2020
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Do vision transformers see like convolutional neural networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith, Chiyuan Zhang, and Alexey Dosovitskiy · 2021
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