Simplicial Neural Networks in a physical application

Bachelor Thesis (2021)
Author(s)

T.C.B. Badier (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

D. Toshniwal – Mentor (TU Delft - Numerical Analysis)

A. W. Heemink – Graduation committee member (TU Delft - Mathematical Physics)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Timon Badier
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Timon Badier
Graduation Date
05-07-2021
Awarding Institution
Delft University of Technology
Programme
Applied Mathematics
Faculty
Electrical Engineering, Mathematics and Computer Science
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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.

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