Geometrisches Maschinelles Lernen
Abstract
In this dissertation we propose an array of novel neural network architectures and optimization algorithms to treat problems in scientific computing, especially reduced order modeling. All the new architectures are structure-preserving (symplectic, volume-preserving, ...) in some sense and we show how these structure-preserving properties can improve the application of neural networks to problems from scientific computing.
Fields of Study & Subjects
Model Reduction and Neural Networks
Neural Networks and Applications
Polynomial and algebraic computation
Publication details
- Journal / source
- mediaTUM (Technical University of Munich)
- Publication date
- 8 January 2026
- Language
- EN
- Document type
- Thesis