The proper generalized decomposition: a powerful tool for model reduction

被引:0
|
作者
Amine Ammar
机构
[1] Laboratoire de Rhéologie,
关键词
Model reduction; Suspension kinetic theory; Multidimensional problems; Proper generalized decomposition (PGD);
D O I
暂无
中图分类号
学科分类号
摘要
One of my best scientific meetings was with Paco Chinesta some years ago. This close partnership has started when he visited Grenoble. He asked me: Can you solve quickly Fokker-Planck equation describing microscopic behaviour of complex fluids?. This question was the beginning of a discovery in the world of the kinetic theory microscopic descriptions. It was also the starting point of the development of efficient numerical techniques reducing simulation costs. Smooth Particle Hydrodynamic was the first release of our attempts. POD and other derived techniques using ‘a priori’ model reduction was the second release. Finally a development of a powerful technique was the main achievement of these works, now known as Proper Generalized Decomposition (PGD). With Paco, we established many collaborations with highly recognized scientists, allowing the proposal of new numerical strategies. Among them I would mention Elias Cueto, Antonio Falco, Etienne Prulière, David Ryckelynck and many others that deserve my acknowledgments. In this paper I’m going to present first the general framework related to the purpose of a fine microscopic description. Then, some details of one of the developed numerical techniques (the PGD) will be presented. Finally, many examples in the kinetic theory description will be addressed for illustrating the possibilities of this powerful technique for solving problems never until now solved.
引用
收藏
页码:89 / 102
页数:13
相关论文
共 50 条
  • [41] Model reduction of input-output dynamical systems by proper orthogonal decomposition
    Or, Arthur C.
    Speyer, Jason L.
    Carlson, Henry A.
    JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2008, 31 (02) : 322 - 328
  • [42] Numerical reduction of a crystallizer model with internal and external coordinates by proper orthogonal decomposition
    Krasnyk, Mykhaylo
    Mangold, Michael
    Ganesan, Sashikumaar
    Tobiska, Lutz
    CHEMICAL ENGINEERING SCIENCE, 2012, 70 : 77 - 86
  • [43] Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition
    Kramer, Boris
    Willcox, Karen E.
    AIAA JOURNAL, 2019, 57 (06) : 2297 - 2307
  • [44] A Krylov enhanced proper orthogonal decomposition method for frequency domain model reduction
    Binion, David
    Chen, Xiaolin
    ENGINEERING COMPUTATIONS, 2017, 34 (02) : 285 - 306
  • [45] Nonlinear Model Order Reduction of Burgers' Equation Using Proper Orthogonal Decomposition
    Abbasi, Farshid
    Mohammadpour, Javad
    2015 AMERICAN CONTROL CONFERENCE (ACC), 2015, : 583 - 588
  • [46] A Krylov enhanced proper orthogonal decomposition method for efficient nonlinear model reduction
    Binion, David
    Chen, Xiaolin
    FINITE ELEMENTS IN ANALYSIS AND DESIGN, 2011, 47 (07) : 728 - 738
  • [47] Automatic Model Reduction of Linear Population Balance Models by Proper Orthogonal Decomposition
    Khlopov, Dmytro
    Mangold, Michael
    IFAC PAPERSONLINE, 2015, 48 (01): : 11 - 16
  • [48] A phase field model for the solid-state sintering with parametric proper generalized decomposition
    Ma, Weixin
    Shen, Yongxing
    POWDER TECHNOLOGY, 2023, 419
  • [49] Solving diffusive equations by proper generalized decomposition with preconditioner
    Tang, Shaoqiang
    Guan, Xinyi
    Liu, Wing Kam
    COMPUTATIONAL MECHANICS, 2024, 73 (01) : 199 - 221
  • [50] Solving diffusive equations by proper generalized decomposition with preconditioner
    Shaoqiang Tang
    Xinyi Guan
    Wing Kam Liu
    Computational Mechanics, 2024, 73 : 199 - 221