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.
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