Related papers: Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks

Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks

URL: http://arxiv.org/abs/2307.11013v1
Date: Thu, 20 Jul 2023 16:38:18 GMT
Title: Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks
Authors: Victor Churchill, Dongbin Xiu
Abstract summary: Flow map learning (FML) is capable of producing accurate predictive models for partially observed systems. We present a set of well defined benchmark problems for learning unknown dynamical systems.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 that it is capable of producing accurate predictive models for partially observed systems, even when their exact mathematical models do not exist. In this paper, we present an overview of the FML framework, along with the important computational details for its successful implementation. We also present a set of well defined benchmark problems for learning unknown dynamical systems. All the numerical details of these problems are presented, along with their FML results, to ensure that the problems are accessible for cross-examination and the results are reproducible.

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