This seminar is organized by the Scientific Computing group of CWI Amsterdam. The focus is on the application of Machine Learning (ML) and Uncertainty Quantification in scientific computing. Topics of interest include, among others:
- combination of data-driven models and (multi scale) simulations
- new ML architectures suited for scientific computing or UQ,
- incorporation of (physical) constraints into data-driven models,
- efficient (online) learning strategies,
- using ML for dimension reduction / creating surrogates,
- inverse problems using ML surrogates,
and any other topic in which some form of ML and/or UQ is used to enhance (existing) scientific computing methodologies. All applications are welcome, be it financial, physical, biological or otherwise.
For more information, or if you'd like to attend one of the talks, please contact Wouter Edeling of the SC group.
Schedule upcoming talks
21 March 2025 10h00: Rafael Bailo (Eindhoven University of Technology): The Collisional Particle-In-Cell Method for the Vlasov-Maxwell-Landau Equations
In this talk we will present an extension of the classical Particle-In-Cell (PIC) method for plasmas which can account for the collisional effects modelled by the Landau operator. The method is derived form the gradient-flow formulation of the Landau equation, thereby preserving the collision invariants and the entropy structure. We will discuss the derivation and implementation of the method, as well as several numerical examples to showcase the effects of collisions in plasma simulations. This is work in collaboration with José Antonio Carrillo (Oxford) and Jingwei Hu (Washington).
16 May 2025 11h00: Anh Khoa Doan (Delft University of Technology):
3 June 2025 11h00: Jemima Tabeart (Eindhoven University of Technology):
17 June 11h00: Ahmed Elgazzar (Donders Institute for Brain, Cognition and Behaviour, Radboud University):