array(2) { ["lab"]=> string(3) "433" ["research"]=> string(3) "728" } DL+Numerical PDE - Deep Learning Beyond CS | LabXing

Deep Learning Beyond CS

DL+Numerical PDE

  • Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, Ken PerlinACCELERATING EULERIAN FLUID SIMULATION WITH CONVOLUTIONAL NETWORKS

    ICLR2017 workshop version:link

  • Jiequn Han, Weinan E.Deep Learning Approximation for Stochastic Control Problems arxiv

    Hoping approximation by neural network can overcome the curse of dimensionality to solve PDE in high dimensional space.

  • Jiequn Han, Arnulf Jentzen, Weinan E.Overcoming the curse of dimensionality: Solving high-dimensional partial differential equations using deep learning arxiv

    longer version.

  • J.Nagoor Kani, Ahmed H. Elsheikh.DR-RNN: A deep residual recurrent neural network for model reduction arXiv

    Designed a physic based RNN with residual connection to do model reduction.(Reduce the dimension for a dynamic.)

  • Weinan E, Bing YuThe Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems. arXiv:1710.00211
  • Christian Beck, Weinan E, Arnulf Jentzen Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations.
  • Masaaki Fujii, Akihiko Takahashi, Masayuki Takahashi.Asymptotic Expansion as Prior Knowledge in Deep Learning Method for high dimensional BSDEs
  • Zichao long, Yiping Lu, Xianzhong Ma, Bin Dong. PDE-Net:Learning PDEs From Data
  • Jens Berg, Kaj NyströmA unified deep artificial neural network approach to partial differential equations in complex geometries

    arXiv

  • Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge

    arXiv

  • Ronan Fablet, Said Ouala, Cedric Herzet Bilinear residual Neural Network for the identification and forecasting of dynamical systems arXiv
  • Y. Khoo, J. Lu, and L. Ying. Solving parametric PDE problems with artificial neural networks. pdf
  • Linfeng Zhang, Han Wang, Weinan E Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic MethodologyarXiv
  • Maziar Raissi, Paris Perdikaris, George Em Karniadakis Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations arXiv2 arXiv(part2)

Ref: http://about.2prime.cn/pde.html (@陆一平-北京大学-数学 )

创建: Apr 27, 2018 | 13:45