Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ1-regularization-Reference-Cited by-同舟云学术

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Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ1-regularization

Published:2020-05 Issue:5 Volume:30 Page:053122
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Yang Q.¹^ORCID,Sing-Long C. A.²^ORCID,Reed E. J.³^ORCID

Affiliation:

1. Computer Science and Engineering Department, University of Connecticut, Storrs, Connecticut 06269, USA

2. Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile

3. Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA

Funder

National Science Foundation

U.S. Department of Energy

Fondo Nacional de Desarrollo Científico y Tecnológico

Publisher

AIP Publishing

Subject

Applied Mathematics,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics

Link

http://aip.scitation.org/doi/am-pdf/10.1063/1.5139463

Reference34 articles.

1. Reduction of dynamical biochemical reactions networks in computational biology

2. A Common Representative Intermediates (CRI) mechanism for VOC degradation. Part 3: Development of a secondary organic aerosol module

3. Chemical kinetics of multiphase reactions between ozone and human skin lipids: Implications for indoor air quality and health effects

4. Toward accommodating realistic fuel chemistry in large-scale computations

5. Turbulence, Coherent Structures, Dynamical Systems and Symmetry

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