Learning Stochastic Dynamics from Data
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.
Cite
Text
Guo et al. "Learning Stochastic Dynamics from Data." ICLR 2024 Workshops: AI4DiffEqtnsInSci, 2024.Markdown
[Guo et al. "Learning Stochastic Dynamics from Data." ICLR 2024 Workshops: AI4DiffEqtnsInSci, 2024.](https://mlanthology.org/iclrw/2024/guo2024iclrw-learning/)BibTeX
@inproceedings{guo2024iclrw-learning,
title = {{Learning Stochastic Dynamics from Data}},
author = {Guo, Ziheng and Zhong, Ming and Cialenco, Igor},
booktitle = {ICLR 2024 Workshops: AI4DiffEqtnsInSci},
year = {2024},
url = {https://mlanthology.org/iclrw/2024/guo2024iclrw-learning/}
}