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.

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.