International Workshop on Data-driven Modeling and Optimization in Fluid Mechanics

The event focuses on the application of artificial intelligence, machine learning, deep learning, evolutional algorithms and adjoint-based optimization to fluid dynamics-related problems with special focus on turbulent flows and flow control.

In particular, the event aims to

provide an opportunity for young researchers to update their knowledge on the application of data-driven methods and be inspired with new ideas;
establish a forum for exchange of ideas between the experts using these methods in different applications;
initiate collaboration and strengthen interdisciplinary research

Organisers

The workshop is organised under the patronage of the "MathSEE" Centre of the Karlsruhe Institut of Technology by

Dr. Pourya Forooghi, Karlsruhe Institute of Technology pourya.forooghi∂kit.edu

Dr. Davide Gatti, Karlsruhe Institute of Technology davide.gatti∂kit.edu

Dr. Mathias Krause, Karlsruhe Institute of Technology mathias.krause∂kit.edu

Dr. Alexander Stroh, Karlsruhe Institute of Technology alexander.stroh∂kit.edu

Key Dates

Workshop: 16-17 September 2019
Speaker's registration deadline: 10 August 2019
Attendee registration deadline: 31 August 2019 (due to limited number of seats, early registration is encouraged)

Venue and Travel Direction

The workshop will be held in the South Campus of the Karlsruhe Institute of Technology, located in the city centre of Karlsruhe in Rudolf-Plank lecture hall Bulding 40.32 (Engler-Bunte-Ring 21, 76131 Karlsruhe).

You can find the location on the map below.

Further information can be found here.

Plenary Talks

The event consists of several plenary talks. The talks will be held in workshop or research seminar format. The list of invited speakers and the titles of the talks are as follow:

Dr. O. Buxton Imperial College London

Title: Multi-scale triple decompostion of multi-scale generated flows usning data-driven techniques

Prof. Y. Hasegawa University of Tokyo

Title: Reconstruction of Turbulent Velocity and Scalar Field Based on Limited Measurements

Prof. P. Koumoutsakos ETH Zürich

Title: Machine learning for fluid mechanics modeling and control

Dr. S. Lerch Karlsruhe Institute of Technology

Title: Probabilistic modeling with machine learning methods

Prof. I. Mortazavi CNAM Paris / Dr. S. Edwige Plastic Omnium

Title: Data assimilation using cross-analysis on experiments and CFD for car aerodynamic optimization

Prof. B. Noack LIMSI, CNRS, Université Paris-Saclay

Title: Turbulence control - Better, faster and easier with machine learning

Prof M. Quadrio Politecnico di Milano

Title: Machine learning and fluid mechanics in biological applications

Dr. J. Riesterer Karlsruhe Institute of Technology

Title: Gaussian process regression for heterogeneous measuring networks of environmental data

Prof. P. Schlatter KTH Stockholm

Title: Data driven wall turbulence: UQ and structures

Prof. V. Schulz Universität Trier

Title: Shape optimization for aerodynamic design

Prof. R. Vinuesa KTH Stockholm

Title: Predictions in turbulent shear flows using deep neural networks

Program

The scientific program can be downloaded here.

Registration

Registration to the workshop is free of charge. However, the number of participants is limited by the capacity of the rooms and our budget. Therefore we kindly ask you to register by sending an email to workshop∂istm.kit.edu with DMOFM as subject. Please indicate your generalities and affiliation in the body of the email.