Deep learning observables in computational fluid dynamics
作者: Kjetil O. LyeSiddhartha MishraDeep Ray
作者单位: 1Seminar for Applied Mathematics (SAM), D-Math, ETH Zürich, Rämistrasse 101, Zürich-8092, Switzerland
2Department of Computational & Applied Mathematics, Rice University, Houston, TX, USA
刊名: Journal of Computational Physics, 2020, Vol.410
来源数据库: Elsevier Journal
DOI: 10.1016/
关键词: CFDDeep learningUQNeural networksObservablesQuasi-Monte Carlo
原始语种摘要: Abstract(#br)Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large number of expensive (forward) numerical solutions of the corresponding PDEs. We propose a machine learning algorithm, based on deep artificial neural networks, that predicts the underlying input parameters to observable map from a few training samples (computed realizations of this map). By a judicious combination of theoretical arguments and empirical observations, we find suitable network architectures and training hyperparameters that result in robust and efficient neural network approximations of the parameters to observable map. Numerical...
全文获取路径: Elsevier  (合作)
影响因子:2.138 (2012)

  • 可观察量 联合收割机
  • learning 学识
  • uncertainty 不定
  • computational 计算的
  • empirical 经验的
  • guaranteed 有保证的
  • combine 联合收割机
  • observable 联合收割机
  • computed 计算的
  • trained 受过训练的
  • dynamics 动力学