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 schemes for 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 approximates the true solution by a linear combination of few products between deterministic orthonormal modes and stochastic modes, both time-dependent.

The first scheme considered consists in a forward discretization in time of both deterministic and stochastic components, in a Euler-Maruyama style.

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, which, on its turn, is shown to be always positive, provided that the SDE under study is driven by a non-degenerate noise.

The second and the third schemes, on the other hand, are staggered ones, alternating updates of the deterministic and the stochastic modes in half steps, and have a projector splitting nature.

We show stability of the second scheme and prove convergence with constants independent of the smallest singular value.

The third scheme works better in practice, although our theoretical convergence bounds are worse than those for the second one.

Computational experiments support our theoretical results.

In this work we do not consider the discretization in probability, which will be the topic of Part II.