Simplicial Neural Networks in a physical application

Bachelor thesis (2021)

Authors

T.C.B. Badier Electrical Engineering, Mathematics and Computer Science

Contributors

D. Toshniwal Numerical Analysis - EEMCS (mentor)
A.W. Heemink Mathematical Physics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Timon Badier
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Published Date

05-07-2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Artificial Intelligence in the form of neural networks is becoming wide spread. This report focuses on a specific form of neural networks, Simplicial Neural Networks. After presenting their advantages and how they were implemented in Python by using the code of [1], they are tested in 2 experiments to explore their applicability in solving physical problems. The first experiment aimed to test the feasibility of the approach as well as to compare them to traditional neural networks. The second experiment aimed at testing the use of SNNs in predicting pressures through a Stokes Flow when the flow is known. Although the experiment was carried out incorrectly the network still produced accurate results in the context of the experiment, and SNNs could present an alternative to Finite Element Solvers.

Files

BEP.pdf
(.pdf | 1.06 Mb)