293 citations · 677 across the 21 of their papers we have counts for
22 papers
Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks
Victor Churchill, Dongbin Xiu
Flow map learning (FML), in conjunction with deep neural networks (DNNs), has shown promises for data driven modeling of unknown dynamical systems. A remarkable feature of FML is t…
Learning Stochastic Dynamical System via Flow Map Operator
Yuan Chen, Dongbin Xiu
We present a numerical framework for learning unknown stochastic dynamical systems using measurement data. Termed stochastic flow map learning (sFML), the new framework is an exten…
Learning Fine Scale Dynamics from Coarse Observations via Inner Recurrence
Victor Churchill, Dongbin Xiu
Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long term prediction of the dynamic…
Deep Learning of Chaotic Systems from Partially-Observed Data
Victor Churchill, Dongbin Xiu
Recently, a general data driven numerical framework has been developed for learning and modeling of unknown dynamical systems using fully- or partially-observed data. The method ut…
Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning
Victor Churchill, Steve Manns, Zhen Chen +1
Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long time prediction of the evoluti…
Modeling unknown dynamical systems with hidden parameters
Xiaohan Fu, Weize Mao, Lo-Bin Chang +1
We present a data-driven numerical approach for modeling unknown dynamical systems with missing/hidden parameters. The method is based on training a deep neural network (DNN) model…