References

HydroBayesCal implements and builds on the following work. Each item is available from its publisher via the DOI/link below. (For developer convenience the project keeps local copies in a git-ignored ExportedItems/ folder; these are not redistributed with the package.)

Core methodology

  • Oladyshkin, S., Mohammadi, F., Kroeker, I., & Nowak, W. (2020). Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory. Entropy, 22(8), 890. doi:10.3390/e22080890

    The Bayesian active-learning strategy (Bayesian model evidence and relative entropy as training-point selection criteria) at the heart of HydroBayesCal.

  • Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. Freely available at gaussianprocess.org/gpml.

    The reference text for the Gaussian-process regression used to build the surrogate models.

Applications

  • Mouris, K., Acuña Espinoza, E., Schwindt, S., Mohammadi, F., Haun, S., Wieprecht, S., & Oladyshkin, S. (2023). Stability Criteria for Bayesian Calibration of Reservoir Sedimentation Models. Modeling Earth Systems and Environment, 9, 3643–3661. doi:10.1007/s40808-023-01712-7

    Surrogate-assisted Bayesian calibration of a 2D hydro-morphodynamic reservoir sedimentation model.

  • Schwindt, S., Callau Medrano, S., Mouris, K., Beckers, F., Haun, S., Nowak, W., Wieprecht, S., & Oladyshkin, S. (2023). Bayesian Calibration Points to Misconceptions in Three-Dimensional Hydrodynamic Reservoir Modeling. Water Resources Research, 59(3), e2022WR033660. doi:10.1029/2022WR033660

    Bayesian calibration of a 3D reservoir hydrodynamic model, showing how posterior geometry reveals faulty model assumptions.

Software dependency

  • BayesValidRox — documentation. Used by HydroBayesCal for the experimental design and parameter sampling (Input and ExpDesigns).