Non-Intrusive Reduced Basis Codes and Models for Surrogate Modelling and Sensitivity Analyses in Magnetotellurics

This code demonstrate the construction of surrogate models for the magnetotelluric response in geothermal reservoirs using the non-intrusive reduced basis method and gaussian process regression presented in the paper “Sensitivity Analysis using Physics-Based Machine Learning: An Example from Surrogate Modelling for Magnetotellurics“ by N. Lindner, D. Degen, A. Grayver and F. Wellmann. The non-intrusive reduced basis method is a physics-based machine learning technique originating from the field of projection based model order reduction methods, and is an efficient way of performing global sensitivity analysis.