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Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation.
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Learning to learn with quantum neural networks via classical neural networks
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Ab initio solution of the many-electron schrödinger equation with deep neural networks
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Flow-based generative models for markov chain monte carlo in lattice field theory
MS Albergo, G Kanwar, and PE Shanahan · 2019
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arXiv preprint arXiv:1909.01377 , 2019
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
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Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 2019
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Samuel J Greydanus, Misko Dzumba, and Jason Yosinski · 2019
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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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Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
Belinda Tzen and Maxim Raginsky · 2019
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Graph networks as a universal machine learning framework for molecules and crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
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Directional message passing for molecular graphs
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Lattice dynamics simulation using machine learning interatomic potentials
VV Ladygin, P Yu Korotaev, AV Yanilkin, and AV Shapeev · 2020
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A short review on rans turbulence models
Siti Nurul Akmal Yusuf, Yutaka Asako, Nor Azwadi Che Sidik, Saiful Bahri Mohamed, and Wan Mohd Arif Aziz Japar · 2020
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Rapid spatiotemporal turbulence modeling with convolutional neural odes
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Language models are few-shot learners
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Self-supervised graph transformer on large-scale molecular data
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Machine learning surrogate models for landau fluid closure
Chenhao Ma, Ben Zhu, Xue-Qiao Xu, and Weixing Wang · 2020
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Statistical mechanics of deep learning
Yasaman Bahri, Jonathan Kadmon, Jeffrey Pennington, Sam S Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli · 2020
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