Skip to content

Latest commit

 

History

History
36 lines (18 loc) · 2.76 KB

DL_Phy.md

File metadata and controls

36 lines (18 loc) · 2.76 KB

Deep Learning For Physics

Deep Learning And PDE

Han J, Jentzen A, Weinan E. Solving high-dimensional partial differential equations using deep learning[J]. Proceedings of the National Academy of Sciences, 2018, 115(34): 8505-8510.

Raissi M, Perdikaris P, Karniadakis G E. Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations[J]. arXiv preprint arXiv:1711.10561, 2017.

Long Z, Lu Y, Ma X, et al. PDE-net: Learning PDEs from data[J]. arXiv preprint arXiv:1710.09668, 2017.

Raissi M. Forward-backward stochastic neural networks: Deep learning of high-dimensional partial differential equations[J]. arXiv preprint arXiv:1804.07010, 2018.

Sun Y, Zhang L, Schaeffer H. NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data[J]. arXiv preprint arXiv:1908.03190, 2019.

Yufei Wang, Ziju Shen, Zichao Long and Bin Dong, Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning, arXiv: 1905.11079, 2019.

Turbulence forecasting via Neural ODE link

System Identification with Time-Aware Neural Sequence Models AAAI2020

Physic Meaningful Embedding

Variational Integrator Networks for Physically Meaningful Embeddings link

Physics-informed Neural Network Architectures

de Bezenac, E., Pajot, A., & Gallinari, P. (2017). Deep learning for physical processes: Incorporating prior scientific knowledge. arXiv preprint arXiv:1711.07970. (ICLR 2018) link

Lutter, M., Ritter, C., & Peters, J. (2019). Deep lagrangian networks: Using physics as model prior for deep learning. arXiv preprint arXiv:1907.04490. (ICLR 2019) link

de Avila Belbute-Peres, F., Smith, K., Allen, K., Tenenbaum, J., & Kolter, J. Z. (2018). End-to-end differentiable physics for learning and control. In Advances in Neural Information Processing Systems (pp. 7178-7189). (NeurIPS 2018) link

Schütt, K., Kindermans, P. J., Felix, H. E. S., Chmiela, S., Tkatchenko, A., & Müller, K. R. (2017). Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. In Advances in Neural Information Processing Systems (pp. 991-1001). (NeurIPS 2018) link

Li Y, Wu J, Tedrake R, et al. Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids[J]. arXiv preprint arXiv:1810.01566, 2018.