Showing stat.MLShow all
3 papers · 1 filter
stat.ML2025
The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
We present a novel kernel-based method for learning multivariate stochastic differential equations (SDEs). The method follows a two-step procedure: we first estimate the drift term…
stat.ML2025
MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions
Victor Rielly, Kamel Lahouel, Ethan Lew +4
Learning a nonparametric system of ordinary differential equations from trajectories in a -dimensional state space requires learning functions of variables. Explicit for…
stat.ML2024
The Stochastic Occupation Kernel Method for System Identification
Michael Wells, Kamel Lahouel, Bruno Jedynak
The method of occupation kernels has been used to learn ordinary differential equations from data in a non-parametric way. We propose a two-step method for learning the drift and d…