Victorita Dolean, Jemima Tabeart, Alexander Heinlein, Tristan van Leeuwen and I are organising a Semester Programme on synergies between machine learning and numerical analysis, with support from CWI. This will take place in Fall 2025 at Centrum Wiskunde & Informatica (Amsterdam). This will be a great opportunity to connect with the international community and learn more about recent developments in the field. The program will features an autumn school (27-31 October), a workshop (1-3 December) and visiting scientists (tba).
Wouter Edeling from my group has organised a Semester Programme on Uncertainty Quantification for High-Dimensional Problems, which took place in Fall 2024 at Centrum Wiskunde & Informatica (Amsterdam). This featured a great opportunity to connect with the international community and learn more about recent developments in the field. The program featured an autumn school (7-11 October), a workshop (11-15 November) and hybrid seminars.
The organizers are Peter Coveney (UvA/University College London), Richard Dwight (TU Delft), Wouter Edeling (CWI), Olga Mula (TU Eindhoven), Laura Scarabosio (Radboud University).
My group has been organising a very successful CWI Semester Programme on Scientific Machine Learning. You can find most of the material here. Here’s a quick overview of our main events:
The future of computational science lies in the combination of physical modelling and data-driven techniques. In my research group at CWI, the Scientific Computing group, we develop new methods and algorithms that enable the transition to truly predictive models. For this purpose, we work at the intersection of different disciplines: uncertainty quantification, partial differential equations and discretization methods, computational fluid dynamics, machine learning, reduced-order modeling, and Bayesian inference.
The models that we develop are applied to a variety of (industrial) applications: for example, sloshing of liquid natural gas in tanker ships, aeroelastic predictions for wind turbines, and transport of multiphase flow in pipelines. Our work also forms an enabler for recent techniques such as Digital Twins.
Sloshing of liquid simulated with smoothed particle hydrodynamics. Courtesy of Yous van Halder.
We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm […]
In turbulence modeling, and more particularly in the Large-Eddy Simulation (LES) framework, we are concerned with finding closure models that […]
Reduced-order models are used widely to make fluid flow simulation computationally tractable for example for the purpose of real-time control [...]
When designing wind turbines, it is necessary to give proof that the turbine will be able to withstand the environmental […]
