Articles | Volume 68
https://doi.org/10.5194/adgeo-68-1-2026
https://doi.org/10.5194/adgeo-68-1-2026
04 Nov 2026
 | 04 Nov 2026

Platypus: a Python package for automated surrogate-assisted analysis of process-based simulations via the non-intrusive reduced-basis method

Jorge Nicolas Hayek, Mauro Cacace, Marthe Faber, Yulia Gruzdeva, and Denise Degen

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Cited articles

Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., and Signell, R. P.: Cloud-Native Repositories for Big Scientific Data, Comput. Sci. Eng., 23, 26–35, https://doi.org/10.1109/MCSE.2021.3059437, 2021. a, b
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA, 2623–2631, ISBN 9781450362016, https://doi.org/10.1145/3292500.3330701, 2019. a
Alla, A., Gräßle, C., and Hinze, M.: Time Adaptivity in Model Predictive Control, J. Sci. Comput., 90, 12, https://doi.org/10.1007/s10915-021-01647-0, 2021. a
Benner, P., Gugercin, S., and Willcox, K.: A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems, SIAM Rev., 57, 483–531, https://doi.org/10.1137/130932715, 2015. a, b
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Short summary
Understanding how reservoirs respond to fluid injection requires complex computer models that are too expensive to run thousands of times for statistical analysis. We developed Platypus, a flexible Python package that automates the workflow from preparing simulations to constructing fast, accurate approximations of expensive simulations. Demonstrated on synthetic and real geothermal scenarios, it makes these analyses more accessible, supporting better decision-making under uncertainty.
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