DynGMA: a robust approach for learning stochastic differential equations from data

arXiv:2402.14475v1 Announce Type: new
Abstract: Learning unknown stochastic differential equations (SDEs) from observed data is a significant and challenging task with applications in various fields. Current approaches often use neural networks to represent drift and diffusion functions, and construct likelihood-based loss by approximating the transition density to train these networks. However, these methods often rely on one-step stochastic numerical schemes, necessitating data with sufficiently high time resolution. In this paper, we introduce novel approximations to the transition density of the …

abstract applications arxiv construct cs.lg cs.na current data differential diffusion drift fields functions likelihood loss math.na networks neural networks physics.comp-ph robust stochastic train transition type