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

Nature Computational Science volume 1, pages 166–168 (2021)Cite this article

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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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Acknowledgements

K.E.W. and O.G. acknowledge US Department of Energy grants DE-SC0019303 and DE-SC0021239. P.H. acknowledges NSF grant #1603903 and funding from NASA/ECCO through a JPL/Caltech subcontract.

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Authors and Affiliations

  1. Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA

    Karen E. Willcox, Omar Ghattas & Patrick Heimbach

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  1. Karen E. Willcox
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  2. Omar Ghattas
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  3. Patrick Heimbach
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Correspondence to Karen E. Willcox.

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Peer review information Nature Computational Science thanks the anonymous reviewers for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this Comment and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Willcox, K.E., Ghattas, O. & Heimbach, P. The imperative of physics-based modeling and inverse theory in computational science. Nat Comput Sci 1, 166–168 (2021). https://doi.org/10.1038/s43588-021-00040-z

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