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Distance Measure Concepts for Bayesian Inference of Chaotic Dynamical System Parameters

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Conference

ASEE 2021 Gulf-Southwest Annual Conference

Location

Waco, Texas

Publication Date

March 24, 2021

Start Date

March 24, 2021

End Date

March 26, 2021

Page Count

16

Permanent URL

https://peer.asee.org/36373

Download Count

14

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Paper Authors

biography

Colin Michael Burdine Baylor University (Student)

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Colin Burdine is a fourth-year undergraduate student at Baylor University, majoring in Mathematics and Computer Science. He is working on his B.S. in Computing with plans to attend graduate school upon graduating in May 2021. He is interested in computer science theory, with applications in randomized algorithms and automated theorem proving.

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Abstract

The problem of determining the parameters of dynamical systems often reduces to developing some notion of a "distance" between observed and simulated system trajectory data. The best parameter fit can then be found by adjusting the parameters that generate the simulated trajectory until the distance is minimized. However, in the case of chaotic dynamical systems, traditional distance measures such as the Mean Square Error (MSE) often fail to produce good results, a consequence of these systems' inherent sensitivity to changes in parameters and initial conditions. In this paper, we adopt the perspective that more robust distance measures can be formulated when the trajectories of these chaotic systems are treated as samples from probability distributions, rather than as time series data. Within this perspective, we evaluate the efficacy of three candidate distance measure concepts: the correlation integral likelihood (proposed by Haario et al.), the Wasserstein metric, and a family of information-theoretic distances based on the Kullback-Leibler divergence. We give particular emphasis to the performance of these methods on the Lorenz63 system, a canonical chaotic system with applications in modeling atmospheric convection.

Burdine, C. M. (2021, March), Distance Measure Concepts for Bayesian Inference of Chaotic Dynamical System Parameters Paper presented at ASEE 2021 Gulf-Southwest Annual Conference, Waco, Texas. https://peer.asee.org/36373

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