Year: 2024
Type: preprint
Abstract: We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm.
Source: arXiv (Cornell University)
Authors: Guo, Ziheng, Cialenco, Igor, Zhong, Ming
Institution: Illinois Institute of Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W4392539647