master thesis projects

Developing Data-Driven Models for Real-Time Dredging Production Optimization

Pre-trained Foundation Models as Fast Proxies for Flow and Transport in Porous Media

Co-Supervisor(s): Denis Voskov (TU Delft, Geoscience and Engineering)

Efficient Poisson Solver for Multi-GPU WaterLily.jl Simulations

Co-Supervisor(s): Bernat Font, Marin Lauber

Physics-Informed Machine Learning for Optical System Calibration

Finite Element Simulation of Multiphase Flow in Porous Media

Co-Supervisor(s): Martin Lesueur

Physics‑Informed LIF Inversion for Full‑Field Temperature and Scalar Transport in Convective Jet Flows

Co-Supervisor(s): Mike van Meerkerk

Learning Preconditioners Using Neural Networks

Co-Supervisor(s): Rui Peng Li (LLNL)

Sparse identification of shape variation dynamics in cardiac anatomy, and generative modeling based on sparse regression

Co-Supervisor(s): Konstantinos Kevopoulos, Mathias Peirlinck

Conditional generative modeling of cardiac anatomy with flow-based architectures

Co-Supervisor(s): Konstantinos Kevopoulos, Mathias Peirlinck

Efficient Inexact Subdomain Solvers for Domain Decomposition Methods with RACE

Co-Supervisor(s): Filipe Cumaru (TU Delft), Dane Lacey, and Gerhard Wellein (FAU)

Several open projects

Co-Supervisor(s): Uwe Iben (Bosch)

Predicting Patient Discharge Dates Using Machine Learning

Co-Supervisor(s): Cindy Pistorius (TU Delft, Optimization)

Graph Neural Network Surrogates to Replace Agent-Based Models for Pandemic Response

Co-Supervisor(s): Martin Kühn (German Aerospace Center & University of Bonn)

Improving Nonlinear Solver Convergence Using Machine Learning

Co-Supervisor(s): Tycho van Noorden (COMSOL)

Exploring new machine learning for wafer alignment

Student: Elena Gijon Ruiz

Neural networks-based surrogate for solving non-linear wafer deformation

Co-Supervisor(s): Ozan Celik (ASML)

Student: Tyren Koning

Machine Learning-Accelerated Solvers for Computational Fluid Dynamics Simulations

Co-Supervisor(s): Merten Stender (TU Berlin)

Student: Ezra Cerpac

Bayesian Inverse Generative Neural Operator — Latent-Space Posterior Formulation for PDE-Constrained Inverse Problems

Co-Supervisor(s): Jing Sun (TU Delft, Intelligent Systems)

Student: Henry Page

Eliminating Interdependencies between Issues for Multi-issue Negotiation

Co-Supervisor(s): Catholijn M. Jonker

Student: Valdimar Þórsson

Operator Learning Using Wavelet Representations

Student: Hangyu Xia

Mode-Decomposition in DeepONets — Generalization and Coupling Analysis

Student: Julius Johannes Taraz

Machine learning for post-storm profile predictions

Co-Supervisor(s): José Antonio Álvarez Antolínez (TU Delft, Hydraulic Engineering), Renske Gelderloos (TU Delft, Environmental Fluid Mechanics), Panos Athanasiou, Kees Nederhoff (Deltares)

Student: Derby Oudenes

Adapting Contrastive Learning Methods for Few-Shot Imputation in High-Dimensional Data

Co-Supervisor(s): Sjoerd Wiarda (ASML)

Student: Michael Henry Aldorf

Creating a machine learning based outlier removal algorithm that incorporates a priori knowledge of the physics

Co-Supervisor(s): Lucas Bekker (ASML)

Student: Thomas Kamminga

Separating longtime behavior and learning of mechanisms

Co-Supervisor(s): Cordula Reisch (TU Braunschweig)

Student: Tirtho Sarathi Saha

Machine learning for post-storm profile predictions

Co-Supervisor(s): José Antonio Álvarez Antolínez, Ad Reniers (TU Delft, Hydraulic Engineering), Panos Athanasiou, and Robert McCall (Deltares)

Student: Koen van Asselt

Estimating congestion and traffic patterns when planning road work

Co-Supervisor(s): Leonoor Portengen, Henk van Haaster (CGI)

Student: Vosse Meijssen

Surrogate models for the characterization of hydrodynamic loads on perforated monopiles

Co-Supervisor(s): Oriol Colomés (TU Delft, Offshore Engineering)

Student: Ruben Dekeyser

Domain Decomposition for Machine Learning Based Medical Imaging

Co-Supervisor(s): Eric Cyr (Sandia National Laboratories, USA)

Student: Corné Verburg

Predicting the Optimal Solver Settings with Machine Learning in COMSOL CFD Module

Co-Supervisor(s): Tycho van Noorden (COMSOL)

Student: Anouk Zandbergen

AI-driven turbulence modeling of two-phase flows in nuclear reactors

Co-Supervisor(s): Edo Frederix (NRG), Deepesh Toshniwal (TU Delft, Numerical Analysis)

Student: Gonzalo Bonilla Moreno

Convolutional Neural Networks for Time-Dependent Fluid Flow

Student: Sylle Hoogeveen

Machine Learning for Turbulent Flows

Co-Supervisor(s): Werner Lazeroms (Sioux)

Student: Elske van Leeuwen

Enhancing Sea Ice Dynamics in the Climate System by Machine Learning

Co-Supervisor(s): Carolin Mehlmann (Otto-von-Guericke University, Max-Planck-Institute of Meteorology)

Student: Brendan Analikwu

Domain Decomposition Techniques for the Helmholtz Equation

Co-Supervisor(s): Vandana Dwarka (TU Delft, Numerical Analysis)

Student: Erik Sieburgh

Surrogate models for the characterization of hydrodynamic loads on perforated monopiles

Co-Supervisor(s): Oriol Colomés (TU Delft, Offshore Engineering)

Student: Quinten Star

The Application of Neural Networks to Predict Skin Evolution After Burn Trauma

Co-Supervisor(s): Fred Vermolen (University of Hasselt)

Student: Selma Husanović

Block Preconditioners for Monolithic Solvers of Very Large Floating Structures

Co-Supervisor(s): Oriol Colomés (TU Delft, Offshore Engineering)

Student: Shreyas Prashanth

Reduced Order Models for Fluid Flow With Generative Adversarial Networks (GANs)

Co-Supervisor(s): Kees Vuik (TU Delft, Numerical Analysis)

Student: Mirko Kemna