From Filtering to Data-Driven Closures: Stabilization Strategies for Convection-dominated Flow Simulations

This PhD thesis investigates advanced stabilization strategies for the simulation of convection-dominated flows. The primary objective is to enhance the numerical stability and accuracy of classical methods through the integration of filtering and correction strategies. The first part of the thesis focuses on the stabilization of marginally-resolved simulations, where insufficient spatial resolution often leads to numerical instabilities, with the use of filtering strategies, in the so-calle...

Quantum Optimal Control and Variational Algorithms for Optimization

Recent advances in quantum technologies have intensified the search for algo rithms that can use quantum resources effectively. Quantum annealing and hybrid quantum–classical variational algorithms are two prominent approaches in this set ting. Conventional quantum annealing follows a prescribed Hamiltonian interpo lation and may therefore be limited by small spectral gaps along that path. This Thesis investigates how algorithmic performance changes when three elements are varied: the a...

Generative Artificial Intelligence and Uncertainty Quantification for Scientific Discovery

Scientific machine learning models can achieve remarkable accuracy, yet fail silently when data are scarce, predictions extend beyond the training distribu- tion, or errors accumulate during deployment. Together these limit their use in settings where an overconfident prediction could yield an unphysical result or misguide an expensive downstream experiment. This thesis investigates whether scientific machine learning models can be designed to recognise what they do not know, while remaining ...

Data-Driven Reduced Order Modelling and Transfer Learning for the Structural Optimization of Cruise Ships

In this work we propose an optimization pipeline for the reduction of the structural mass of passenger cruise ships in the preliminary design phase. In simulation-driven design, the large cost of high-fidelity evaluations can be mitigated by leveraging data-driven reduced order models, exploring the design space via multi- and single-objective optimization of the surrogates and only validating the most promising candidates. To tackle the complexity in the formulation of effective optimiza...

Coherent statistics of branched polymers and double-folded rings Exact results and algorithms

Data‐driven coordinate discovery and causal inference in molecular simulations

Molecular simulations generate high-dimensional trajectories in which the mechanistically relevant features are buried across widely separated time scales ranging from collective structural motions that govern, for example, protein fluctuations to electronic transitions induced by light absorption in photochemistry. While such simulations capture the underlying atomic and electronic dynamics in fine detail, the cause-and-effect relationships that drive these types of processes remain poorly u...

Parameterized Orthotropic Plate Systems: Numerical Methods and Model Reduction — From High-Fidelity C⁰ Interior-Penalty Discretizations to Reduced-Order Models for Plates on Elastic Foundations

This dissertation develops and evaluates a unified high-fidelity and reduced-order framework for parameterized thin orthotropic Kirchhoff–Love plates. Motivated primarily by monolithic and panelized flooring systems on compliant foundations, the formulations also apply to plates with parameter-dependent material, support, loading, thermal, and dynamic properties. Three connected model classes are considered: static mechanics with unilateral plate–foundation interaction, steady thermomechanica...

Towards fast and robust Large Scale Structure analysis with physics-based emulators

Asymmetric Response of Selected RNAs to Forced NanoporeTranslocation