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From Data to Dynamics: Discovering Governing Equations from Data

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서명/저자사항From Data to Dynamics: Discovering Governing Equations from Data.
개인저자Champion, Kathleen.
단체저자명University of Washington. Applied Mathematics.
발행사항[S.l.]: University of Washington., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항109 p.
기본자료 저록Dissertations Abstracts International 81-04B.
Dissertation Abstract International
ISBN9781687947192
학위논문주기Thesis (Ph.D.)--University of Washington, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Kutz, J. Nathan.
이용제한사항This item must not be sold to any third party vendors.This item must not be added to any third party search indexes.
요약Governing laws and equations, such as Newton's second law for classical mechanics and the Navier-Stokes equations thence derived, have been responsible throughout history for numerous scientific breakthroughs in the physical and engineering sciences. There are many systems of interest for which large quantities of measurement data have been collected, but the underlying governing equations remain unknown. While machine learning approaches such as sparse regression and deep neural networks have been successful at discovering governing laws and reduced models from data, many challenges still remain. In this work, we focus on the discovery of nonlinear dynamical systems models from data. We present several methods based on the sparse identification of nonlinear dynamics (SINDy) algorithm. These approaches address a number of challenges that occur when dealing with scientific data sets, including unknown coordinates, multiscale dynamics, parametric dependencies, and outliers. Our methods focus on discovering parsimonious models, as parsimony is key for obtaining models that have physical interpretations and can generalize to predict previously unobserved behaviors.
일반주제명Applied mathematics.
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