A multi-dimensional adaptive sampling algorithm and its application to Fermi surfaces

Bachelor thesis (2019)

Authors

J.R. Hoofwijk Applied Sciences

Contributors

A.R. Akhmerov QN/Akhmerov Group - AS (supervisor 1)
D. den Ouden-van der Horst Numerical Analysis - EEMCS (supervisor 1)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2019 Jorn Hoofwijk
Adaptive sampling Fermi surface Triangulation Adaptive
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Published Date

03-07-2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The aim of this research is to develop an N -dimensional adaptive sampling algorithm to efficiently sample functions, meaning that with fewer samples the same accuracy is achieved compared to what homogeneously spaced samples would achieve. This algorithm is based on an existing Python package called Adaptive. The developed algorithm is applied to find and plot the Fermi surface of crystals with a higher resolution than homogeneous sampling would with the same number of points.