A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models that can (i) generalize to predict previously uno...

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Bibliographic Details
Main Authors: Kathleen Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Sparse regression
systems identification
outlier removal
nonconvex optimization
Online Access:https://ieeexplore.ieee.org/document/9194760/

Internet

https://ieeexplore.ieee.org/document/9194760/

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