Artificial Intelligence and Machine Learning

A team of researchers led by TU Ilmenau Professor Jörg Schumacher have been using the JUWELS supercomputer at the Jülich Supercomputing Centre to run highly detailed direct numerical simulations (DNS) of turbulent flows at the so-called mesoscale—the intermediate range where both small-scale turbulent fluid interactions and large-scale fluid dynamics converge.

A group of researchers from the Fritz Haber Institute and Aarhus University in Denmark have leveraged the power of the JUWELS supercomputer at the Jülich Supercomputing Centre (JSC) to develop a machine learning algorithm that helps predict how specific molecules bind to the surface of a catalyst. Catalysts play an essential role in many chemical processes, and how specific molecules interact with these materials can influence the efficiency, effectiveness, and safety of chemical reactions at an industrial scale.

Impressive progress has recently been made in machine learning where learning capabilities at (super-)human level can now be produced in non-spiking artificial neural networks. A critical challenge for machine learning is the large number of samples required for training. This project investigated new high-throughput methods across various domains for biologically based spiking neuronal networks. Sub-projects explored tools and learning algorithms to study and enhance learning performance in biological neural networks and to equip variants of data driven models with fast learning capabilities. Applications of these learning techniques in neuromorphic hardware and design for their future application in neurorobotics were also included.

The need to reduce the skin-friction drag of aerodynamic vehicles is of paramount importance. Nominally 50% of the total energy consumption of an aircraft or high-speed train is due to skin-friction drag. Reducing skin-friction drag reduces fuel consumption and transport emissions, leading to vast economic savings and wider health and environmental benefits. In this project, wall-normal blowing is combined with a Bayesian Optimisation framework in order to find the optimal parameters to generate net energy savings over a turbulent boundary layer. It is found that wall-normal blowing with amplitudes of less than 1% of the freestream velocity of the boundary layer can generate a drag reduction of up to 80% with up to 5% of energy saving.