Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7375-2023
https://doi.org/10.5194/gmd-16-7375-2023
Review and perspective paper
 | 
19 Dec 2023
Review and perspective paper |  | 19 Dec 2023

Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations

Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann

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Cited articles

Abdi, D. S., Wilcox, L. C., Warburton, T. C., and Giraldo, F. X.: A GPU-accelerated continuous and discontinuous Galerkin non-hydrostatic atmospheric model, Int. J. High Perform. C., 33, 81–109, https://doi.org/10.1177/1094342017694427, 2017. a
Adams, B., Bohnhoff, W., Dalbey, K., Ebeida, M., Eddy, J., Eldred, M., Hooper, R., Hough, P., Hu, K., Jakeman, J., Khalil, M., Maupin, K., Monschke, J., Ridgway, E., Rushdi, A., Seidl, D., Stephens, J., Swiler, L., and Winokur, J.: DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.12 User's Manual, Sandia National Laboratories, Tech. Rep., SAND2020-12495, 2020. a, b, c
Afanasyev, A., Bianco, M., Mosimann, L., Osuna, C., Thaler, F., Vogt, H., Fuhrer, O., VandeVondele, J., and Schulthess, T. C.: GridTools: A framework for portable weather and climate applications, Software X, 15, 100707, https://doi.org/10.1016/j.softx.2021.100707, 2021. a
Alexander, F., Almgren, A., Bell, J., Bhattacharjee, A., Chen, J., Colella, P., Daniel, D., DeSlippe, J., Diachin, L., Draeger, E., Dubey, A., Dunning, T., Evans, T., Foster, I., Francois, M., Germann, T., Gordon, M., Habib, S., Halappanavar, M., Hamilton, S., Hart, W., Huang, Z. H., Hungerford, A., Kasen, D., Kent, P. R. C., Kolev, T., Kothe, D. B., Kronfeld, A., Luo, Y., Mackenzie, P., McCallen, D., Messer, B., Mniszewski, S., Oehmen, C., Perazzo, A., Perez, D., Richards, D., Rider, W. J., Rieben, R., Roche, K., Siegel, A., Sprague, M., Steefel, C., Stevens, R., Syamlal, M., Taylor, M., Turner, J., Vay, J.-L., Voter, A. F., Windus, T. L., and Yelick, K.: Exascale applications: skin in the game, Philos. T. Roy. Soc. A, 378, 20190056, https://doi.org/10.1098/rsta.2019.0056, 2020. a, b
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Executive editor
This manuscript provides a review of physics-based machine learning methods, and provides a perspective on their use.
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.