A multi-dimensional adaptive sampling algorithm and its application to Fermi surfaces
J.R. Hoofwijk (TU Delft - Applied Sciences)
AR Akhmerov – Mentor (TU Delft - QN/Akhmerov Group)
D den Ouden-van der Horst – Mentor (TU Delft - Numerical Analysis)
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The Github repository where one may find the source code of which my thesis is a part
https://github.com/python-adaptive/adaptiveOther than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.