Non-Intrusive Reduced Basis Method - Case Study of the Alpine Region

This repository contains the scripts and data files required for the surrogate model constructions as described in the paper “Challenges of Quantifying Geodynamic Effects – Insights from the Alpine Region “ by Denise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, and Mauro Cacace. The non-intrusive reduced basis method is a physics-based machine learning technique originating from the field of projection based model order reduction methods. The repository contains three main folders: - Data: Containing the training and validation data, as well as the training and validation parameters for all investigated scenarios. - SurrogateModels: Containing the surrogate models for all scenarios investigated. - SurrogateModelScripts: Containing the scripts to construct all used surrogate models.