The following repository provides a tool to do a stochastic calibration of computationally expensive models using a surrogated assisted bayesian inversion technique proposed by Oladyshkin et al (2020). The implementation of the code is done in Python, but a MATLAB version by Oladyshkin can be found as BAL-GPE Matlab Toolbox.
The repository is organized as follows:
- AnalyticalFunctionExample: In this folder an example of the metod is implemented with an analytical function
- GPE_BAL_Telemac: In this folder the calibration methodology is coupled with TELEMAC 2D and GAIA to calibrate a 2D hydromorphodynamic model for reservoir sedimentantion
- run_multiple_Telemac: In the folder a function to run Telemac from python multiple times is presented. This function is useful when one is creating the initial surrogate model (also known as metamodel) that will later be updated through the Bayesian Active Learning technique.
All folders are independent of each other and a detailed explanation of the files can be found inside each folder.
No warranty is expressed or implied regarding the usefulness or completeness of the information and documentation provided. References to commercial products do not imply endorsement by the Authors. The concepts, materials, and methods used in the algorithms and described in the documentation are for informational purposes only. The Authors has made substantial effort to ensure the accuracy of the algorithms and the documentation, but the Authors shall not be held liable, nor his employer or funding sponsors, for calculations and/or decisions made on the basis of application of the scripts and documentation. The information is provided "as is" and anyone who chooses to use the information is responsible for her or his own choices as to what to do with the data. The individual is responsible for the results that follow from their decisions.
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- Eduardo Acuna
- Kilian Mouris
- Sebastian Schwindt
- Farid Mohammadi
For question you can write an email to eduardo.espinoza@kit.edu