TetraFEM: Numerical Solution of Partial Differential Equations Using Tensor Train Finite Element Method

In this paper, we present a methodology for the numerical solving of partial differential equations in 2D geometries with piecewise smooth boundaries via finite element method (FEM) using a Quantized Tensor Train (QTT) format. During the calculations, all the operators and data are assembled and represented in a compressed tensor format. We introduce an efficient assembly procedure of FEM matrices in the QTT format for curvilinear domains. The features of our approach include efficiency in terms of memory consumption and potential expansion to quantum computers. We demonstrate the correctness and advantages of the method by solving a number of problems, including nonlinear incompressible Navier–Stokes flow, in differently shaped domains.

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Publication Details

Journal
Mathematics
Published
2024-10-18
DOI
https://doi.org/10.3390/math12203277
Citations
5
Primary Topic
Numerical methods for differential equations
Type
article
Field-Weighted Citation Impact
3.66
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article

TetraFEM: Numerical Solution of Partial Differential Equations Using Tensor Train Finite Element Method

Egor Kornev, Sergey Dolgov, Michael Romanovich Perelshtein, Artem Melnikov
5 citations
Mathematics
Numerical methods for differential equations
3.66
article

TetraFEM: Numerical Solution of Partial Differential Equations Using Tensor Train Finite Element Method

Egor Kornev, Sergey Dolgov, Michael Romanovich Perelshtein, Artem Melnikov
article en
5 citations

Abstract

In this paper, we present a methodology for the numerical solving of partial differential equations in 2D geometries with piecewise smooth boundaries via finite element method (FEM) using a Quantized Tensor Train (QTT) format. During the calculations, all the operators and data are assembled and represented in a compressed tensor format. We introduce an efficient assembly procedure of FEM matrices in the QTT format for curvilinear domains. The features of our approach include efficiency in terms of memory consumption and potential expansion to quantum computers. We demonstrate the correctness and advantages of the method by solving a number of problems, including nonlinear incompressible Navier–Stokes flow, in differently shaped domains.

MathematicsVol. 12(20)
Terra Quantum (Switzerland) (CH), University of Bath (GB)
Openalex Percentile: Top 6%
Numerical methods for differential equations
3.66
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TetraFEM: Numerical Solution of Partial Differential Equations Using Tensor Train Finite Element Method — Egor Kornev, Sergey Dolgov, et al. · Mathematics (2024) | TGRS Research Map | TGRS