We propose a numerical method for discovering unknown parameterized dynamical systems by using observational data of the state variables. Our method is built upon and extends the recent work of discovering unknown dynamical systems, in particular those using deep neural network (DNN). We propose a DNN structure, largely based upon the residual network (ResNet), to not only learn the unknown form of the governing equation but also take into account the random effect embedded in the system, which is generated by the random parameters. Once the DNN model is successfully constructed, it is able to produce system prediction over longer term and for arbitrary parameter values. For uncertainty quantification, it allows us to conduct uncertainty analysis by evaluating solution statistics over the parameter space.
@article{arxiv.1910.07096,
title = {Deep learning of parameterized equations with applications to uncertainty quantification},
author = {Tong Qin and Zhen Chen and John Jakeman and Dongbin Xiu},
journal= {arXiv preprint arXiv:1910.07096},
year = {2020}
}