Discovering stochastic dynamical equations from biological time series data

arXiv:2205.02645v5 Announce Type: replace-cross
Abstract: Stochastic differential equations (SDEs) are an important framework to model dynamics with randomness, as is common in most biological systems. The inverse problem of integrating these models with empirical data remains a major challenge. Here, we present an equation discovery methodology that takes time series data as an input, analyses fine scale fluctuations and outputs an interpretable SDE that can correctly capture long-time dynamics of data. We achieve this by combining traditional approaches from stochastic …

abstract arxiv challenge cs.lg data differential discovery dynamics equation framework major math.ds methodology q-bio.qm randomness series stochastic systems time series type