Numerical Methods for Dynamical Low-Rank Approximations of Stochastic Differential Equations -- Part I: Time discretization

In this work (Part I), we study three time-discretization procedures of the Dynamical Low-Rank Approximation (DLRA) of high-dimensional stochastic differential equations (SDEs). Specifically, we consider the Dynamically Orthogonal (DO) method for DLRA proposed and analyzed in arXiv:2308.11581v4, which consists of a linear combination of products between deterministic orthonormal modes and stochastic modes, both time-dependent. The first strategy we consider for numerical time-integration is very standard, consisting in a forward discretization in time of both deterministic and stochastic components. Its convergence is proven subject to a time-step restriction dependent on the smallest singular value of the Gram matrix associated to the stochastic modes. Under the same condition on the time-step, this smallest singular value is shown to be always positive, provided that the SDE under study is driven by a non-degenerate noise. The second and the third algorithms, on the other hand, are staggered ones, in which we alternately update the deterministic and the stochastic modes in half steps. These approaches are shown to be more stable than the first one and allow us to obtain convergence results without the aforementioned restriction on the time-step. Computational experiments support theoretical results. In this work we do not consider the discretization in probability, which will be the topic of Part II.

Source: arXiv:2601.21428v1 - http://arxiv.org/abs/2601.21428v1 PDF: https://arxiv.org/pdf/2601.21428v1 Original Link: http://arxiv.org/abs/2601.21428v1