Model reduction of multidimensional dynamical systems by tensor decompositions

نویسنده

  • Jaron Achterberg
چکیده

This paper investigates different methods for multi dimensional POD reduction. The POD basis functions will be obtained from a tensor decomposition method. Multiple methods are available and will be compared for their use in model reduction. These methods will be applied to a dynamical system described by Partial Differential Equations, PDEs. The methods are well defined on functions on Cartesian domains, several methods to apply them on non Cartesian domains are treated as well.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Space-time least-squares Petrov-Galerkin projection for nonlinear model reduction

This work proposes a space–time least-squares Petrov–Galerkin (ST-LSPG) projection method for model reduction of nonlinear dynamical systems. In contrast to typical nonlinear model-reduction methods that first apply (Petrov– )Galerkin projection in the spatial dimension and subsequently apply time integration to numerically resolve the resulting low-dimensional dynamical system, the proposed me...

متن کامل

Numerical approximation of multi-dimensional PDEs in quantized tensor spaces

Modern methods of tensor-product approximation by separation of variables allow an efficient lowparametric calculus of functions and operators in higher dimensions. Most common separable representations combine the Tucker, canonical, tensor train (TT) and the more general MPS-type decompositions. The idea of data quantization makes it possible to represent (approximate) the multivariate functio...

متن کامل

Tensor modeling and signal processing for wireless communication systems. (Modélisation et traitement tensoriel du signal pour les systèmes de communication sans-fil)

In several signal processing applications for wireless communications, the received signal is multidimensional in nature and may exhibit a multilinear algebraic structure. In this context, the PARAFAC tensor decomposition has been the subject of several works in the past six years. However, generalized tensor decompositions are necessary for covering a wider class of wireless communication syst...

متن کامل

Era of Big Data Processing: A New Approach via Tensor Networks and Tensor Decompositions

Many problems in computational neuroscience, neuroinformatics, pattern/image recognition, signal processing and machine learning generate massive amounts of multidimensional data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide often a natural and compact representation for such massive multidimensional data via suitable low-rank approximations. Big data a...

متن کامل

Tensor Decompositions for Very Large Scale Problems

Modern applications such as neuroscience, text mining, and large-scale social networks generate massive amounts of data with multiple aspects and high dimensionality. Tensors (i.e., multi-way arrays) provide a natural representation for such massive data. Consequently, tensor decompositions and factorizations are emerging as novel and promising tools for exploratory analysis of multidimensional...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017