Prof. Dr. rer. nat. habil. Jörg Schumacher

Institute of Thermodynamics and Fluid Mechanics,
TU Ilmenau, Germany

Short bio:

Professor Jörg Schumacher is a Professor of Fluid Mechanics at the Technische Universität Ilmenau. He earned his Diploma in Physics from Philipps University Marburg and his PhD in Theoretical Physics from the University of Potsdam. After postdoctoral research in Marburg and at Yale University, he joined TU Ilmenau in 2005 as Professor for Theoretical Fluid Mechanics and has been a full Professor of Fluid Mechanics since 2013.

Professor Schumacher's research focuses on turbulent fluid flows, including turbulent convection, magnetohydrodynamics, and advanced numerical simulation methods, as well as machine learning and quantum computing applications in fluid dynamics. He has received prestigious awards including a Feodor Lynen Fellowship, a Heisenberg Professorship, the Thuringian Research Award, and an ERC Advanced Grant. In 2021 he was elected a Fellow of the American Physical Society and was granted an Excellence Project of the John von Neumann Institute for Computing in 2022.

Short abstract: 

Quantum computing opens new ways to process data and build reduced-order models of the complex nonlinear dynamics in turbulent fluid flows. The description of fluid dynamics typically proceeds in the Eulerian frame of reference – flow fields are observed in a laboratory frame fixed in space. The complementary Lagrangian frame of reference is attached to infinitesimal fluid parcels while moving through the fluid flow. The latter is relevant when, e.g,, the turbulent mixing of substances or chemical species in fluid flows is analysed. In my presentation, two routes of data-driven modeling will be mostly discussed: 

(1) Quantum reservoir computing, a hybrid quantum-classical machine learning algorithm that is designed to process time-sequential data. This class of algorithms is applied to model buoyancy-driven fluid flows in the Eulerian frame of reference. 

(2) The sampling in a high-dimensional data space by a Quantum-enhanced Markov chain Monte Carlo method is applied to build a hybrid quantum-classical stochastic model of the dispersion of tracers in a turbulent shear flow. 

Current limitations of both methods and possible future extensions will be also discussed. This work is supported by the Carl-Zeiss Foundation, the Deutsche Forschungsgemein-schaft (DFG) and the European Research Council (ERC).