We consider the problem of variational Bayesian inference in a latent variable model where
a (possibly complex) observed stochastic process is governed by the unobserved solution of …

We identify effective stochastic differential equations (SDEs) for coarse observables of fine-
grained particle-or agent-based simulations; these SDEs then provide useful coarse …

The discovery of governing differential equations from data is an open frontier in machine
learning. The sparse identification of nonlinear dynamics (SINDy)\citep …

Partial observations of continuous time-series dynamics at arbitrary time stamps exist in
many disciplines. Fitting this type of data using statistical models with continuous dynamics …

Hybrid energy harvesting systems are broadly applied in various fields due to the advantage
of improving energy harvesting efficiency. In actual environment, there are many complex …

State-space models (SSMs) offer a powerful framework for dynamical system analysis,
wherein the temporal dynamics of the system are assumed to be captured through the …

Discovering the underlying relationships among variables from temporal observations has
been a longstanding challenge in numerous scientific disciplines, including biology, finance …

We present a numerical method to learn an accurate predictive model for an unknown
stochastic dynamical system from its trajectory data. The method seeks to approximate the …

We construct a reduced, data-driven, parameter dependent effective stochastic differential
equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from …

McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical
description of the behavior of an infinite number of interacting particles by imposing a …