.png){kind=icon}
.png){kind=icon}
Return
A robust and conservative dynamical low-rank algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.jcp.2023.112060.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Dynamical low-rank approximation, as has been demonstrated recently, can be extremely efficient in solving kinetic equations. However, a major deficiency is that it does not preserve the structure of the underlying physical problem. For example, the classic dynamical low-rank methods violate mass, momentum, and energy conservation. In Einkemmer and Joseph (2021) [9] a conservative dynamical low-rank approach has been proposed. However, directly integrating the resulting equations of motion, similar to the classic dynamical low-rank approach, results in an ill-posed scheme. In this work we propose a robust, i.e. well-posed, low-rank integrator that conserves mass and momentum (up to machine precision) and significantly improves energy conservation. We also report improved qualitative results for some problems and show how the approach can be combined with a rank adaptive scheme.(c) 2023 Elsevier Inc. All rights reserved.
Keywords:
Dynamical low-rank approximation
Conservative methods
Structure preserving numerical methods
Complexity reduction
High-dimensional problems
Kinetic equations
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
1.5W
Citations:
7.4W
