The imperative of physics-based modeling and inverse theory in computational science

To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling.

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Veröffentlicht in:Nature Computational Science Jg. 1; H. 3; S. 166 - 168
Hauptverfasser: Willcox, Karen E., Ghattas, Omar, Heimbach, Patrick
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Nature Publishing Group 01.03.2021
Schlagworte:
Artificial intelligence
Big Data
Complex systems
Engineering
Geometry
Gravitational waves
Inverse problems
Machine learning
Partial differential equations
Physics
Power
Science
Simulation
Sparsity
ISSN:2662-8457, 2662-8457
Online-Zugang:Volltext
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Zusammenfassung:To best learn from data about large-scale complex systems, physics-based models representing the laws of nature must be integrated into the learning process. Inverse theory provides a crucial perspective for addressing the challenges of ill-posedness, uncertainty, nonlinearity and under-sampling.
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ISSN:2662-8457
2662-8457
DOI:10.1038/s43588-021-00040-z