Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism
基于Mori-Zwanzig形式主义的参数POD- Galerkin降阶模型的递归神经网络封闭
作者: Qian WangNicolò RipamontiJan S. Hesthaven
作者单位: 1Chair of Computational Mathematics and Simulation Science, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
刊名: Journal of Computational Physics, 2020, Vol.410
来源数据库: Elsevier Journal
DOI: 10.1016/
关键词: Memory closurePOD-GalerkinModel reductionConditioned long-short term memoryImplicit-explicit Runge-Kutta
原始语种摘要: Abstract(#br)Closure modeling based on the Mori-Zwanzig formalism has proven effective to improve the stability and accuracy of projection-based model order reduction. However, closure models are often expensive and infeasible for complex nonlinear systems. Towards efficient model reduction of general problems, this paper presents a recurrent neural network (RNN) closure of parametric POD-Galerkin reduced-order model. Based on the short time history of the reduced-order solutions, the RNN predicts the memory integral which represents the impact of the unresolved scales on the resolved scales. A conditioned long short term memory (LSTM) network is utilized as the regression model of the memory integral, in which the POD coefficients at a number of time steps are fed into the LSTM units,...
全文获取路径: Elsevier  (合作)
影响因子:2.138 (2012)

  • parametric 子宫旁的
  • formalism 形式方法
  • closure 闭合
  • neural 神经系统的
  • reduced 减缩的
  • implicitly 隐含地
  • POD Power On Diagnostics
  • network 网络
  • memory 记忆
  • integrated 综[组,复]合