Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems-Reference-Cited by-同舟云学术

Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems

Published:2020-05 Issue:5 Volume:30 Page:053111
ISSN:1054-1500
Container-title:Chaos: An Interdisciplinary Journal of Nonlinear Science
language:en
Short-container-title:Chaos

Author:

Wikner Alexander¹^ORCID,Pathak Jaideep¹^ORCID,Hunt Brian²³,Girvan Michelle¹²^ORCID,Arcomano Troy⁴^ORCID,Szunyogh Istvan⁴^ORCID,Pomerance Andrew⁵,Ott Edward¹⁶^ORCID

Affiliation:

1. Department of Physics and Institute for Research in Electronics and Applied Physics, University of Maryland, College Park, Maryland 20740, USA

2. Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20740, USA

3. Department of Mathematics, University of Maryland, College Park, Maryland 20740, USA

4. Department of Atmospheric Sciences, Texas A&M University, College Station, Texas 77843, USA

5. Potomac Research LLC, Alexandria, Virginia 22311, USA

6. Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland 20740, USA

Funder

Defense Advanced Research Projects Agency

Publisher

AIP Publishing

Subject

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

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0005541

Reference19 articles.

1. Deep learning

2. The quiet revolution of numerical weather prediction

3. Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

4. Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

5. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

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