Learning Stochastic Dynamics from Data

Ziheng Guo, Ming Zhong, Igor Cialenco

Keywords: Stochastic Dynamics, Random Noise, System Identification

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 components of the noise are 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.

Submission Number: 87