Data-driven discovery of partial differential equations-Reference-Cited by-同舟云学术

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Data-driven discovery of partial differential equations

Published:2017-04-07 Issue:4 Volume:3 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Rudy Samuel H.¹,Brunton Steven L.²,Proctor Joshua L.³,Kutz J. Nathan¹

Affiliation:

1. Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA.

2. Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.

3. Institute for Disease Modeling, 3150 139th Avenue Southeast, Bellevue, WA 98005, USA.

Abstract

Researchers propose sparse regression for identifying governing partial differential equations for spatiotemporal systems.

Funder

Air Force Office of Scientific Research

Defense Advanced Research Projects Agency

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference50 articles.

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3. Identification of distributed parameter systems: A neural net based approach;González-García R.;Comput. Chem. Eng.,1998

4. Amplitude equations from spatiotemporal binary-fluid convection data;Voss H. U.;Phys. Rev. Lett.,1999

5. Detecting Causality in Complex Ecosystems

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