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arXiv 2201.13391math.NAcs.NAmath.DGmath.DSmath.PR
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随机哈密顿系统的数据驱动保结构模型降阶

Data-driven structure-preserving model reduction for stochastic Hamiltonian systems

Tomasz M. Tyranowski

AI总结:

针对随机哈密顿系统高维度及大量Monte Carlo模拟计算成本高的问题,将基于SVD的模型降阶技术和本征辛分解方法推广至该类系统,经数值实验验证其保结构特性可提升解的精度、稳定性与能量守恒效果。

摘要:

本研究表明,适用于常微分方程的基于SVD的模型降阶技术(如本征正交分解)可推广至随机微分方程,以降低因所研究随机系统的高维度和大量独立Monte Carlo运行带来的计算成本。我们还将本征辛分解方法推广到有外力和无外力作用的随机哈密顿系统,并论证保留底层辛结构或变分结构能得到更精确、稳定的解,其能量守恒效果优于使用非几何方法的情况。我们通过随机非线性薛定谔方程的半离散化和Kubo振子的数值实验验证了所提出的技术。

英文摘要:

In this work we demonstrate that SVD-based model reduction techniques known for ordinary differential equations, such as the proper orthogonal decomposition, can be extended to stochastic differential equations in order to reduce the computational cost arising from both the high dimension of the considered stochastic system and the large number of independent Monte Carlo runs. We also extend the proper symplectic decomposition method to stochastic Hamiltonian systems, both with and without external forcing, and argue that preserving the underlying symplectic or variational structures results in more accurate and stable solutions that conserve energy better than when the non-geometric approach is used. We validate our proposed techniques with numerical experiments for a semi-discretization of the stochastic nonlinear Schrödinger equation and the Kubo oscillator.

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