SDL Fluids presents

Introduction to Kernel-based approximation methods with applications to fluid dynamics

Date: 28.05.2024 1.00 pm - 5.00 pm; Format: hybrid

Short abstract:

When data is provided in an unstructured format or is high-dimensional, classical interpolation or approximation schemes as Finite-Element Methods (FEM) struggle to be accurate and efficient. An alternative is provided by kernel-based approaches in the Reproducing Kernel Hilbert space (RKHS) framework. Common applications range from support vector machines in context of machine learning to reconstruction of image data. In this course, we will introduce the framework for kernel-based approximation schemes and discuss how one can implement them efficiently. At the end we will look at possible applications of kernel-based methods in context of fluid dynamics and particle methods.

Information about SDL Fluids


Language: English

Capacity: in person tbd, no limit for online participants

Location: Seminar 328 in Rogowski building
Schinkelstr. 2 52062, Aachen, Germany

Prerequisites: tba

Further information: additional information will be announced to the participants in the week before the course.

Please, note that this training will be held online and in person. Register accordingly!

Click here to register for on site

Click here to register for online


As well open for the participants of
the European Digital Innovation Hub

Contact person

Rostislav-Paul Wilhelm

RWTH Aachen University