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

Author:

Degen DeniseORCID,Caviedes Voullième DanielORCID,Buiter SusanneORCID,Hendricks Franssen Harrie-Jan,Vereecken HarryORCID,González-Nicolás Ana,Wellmann FlorianORCID

Abstract

Abstract. An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.

Funder

RWTH Aachen University

Bundesministerium für Bildung und Forschung

Publisher

Copernicus GmbH

Subject

General Medicine

Reference228 articles.

1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/ (last access: 24 September 2023), 2015. a

2. 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

3. 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

4. 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

5. 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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