Data-driven discovery of partial differential equations

Samuel H. Rudy; Steven L. Brunton; Joshua L. Proctor; J. Nathan Kutz

doi:10.1126/sciadv.1602614

journal article Apr 07, 2017

Data-driven discovery of partial differential equations

Samuel H. Rudy Steven L. Brunton

Joshua L. Proctor J. Nathan Kutz

Science Advances Vol. 3 No. 4 · American Association for the Advancement of Science (AAAS)

View at Publisher Save 10.1126/sciadv.1602614

Abstract

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

Topics

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Metrics

1,201

Citations

50

References

Details

Published: Apr 07, 2017
Vol/Issue: 3(4)

Authors

S

Samuel H. Rudy

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

S

Steven L. Brunton

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

J

Joshua L. Proctor

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

J

J. Nathan Kutz

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

Funding

Defense Advanced Research Projects Agency Award: ID0EARBG14924

Air Force Office of Scientific Research Award: ID0EXLBG14923

Cite This Article

Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, et al. (2017). Data-driven discovery of partial differential equations. Science Advances, 3(4). https://doi.org/10.1126/sciadv.1602614

Data-driven discovery of partial differential equations

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