Claudia Drygala is a research assistant and doctoral candidate at the Chair of Mathematical Modeling of Industrial Life Cycles at TU Berlin. She completed her mathematics studies in 2020 at the University of Wuppertal, where she subsequently worked as a research assistant in the stochastics working group. Since October 2022, she has been associated with her research project in the DataNinja NRW Research Training Group. In her research, she deals with generative learning methods for problems in the field of autonomous driving and fluid dynamics, in particular to reduce the annotation and computational cost.

Hanno Gottschalk is Professor for Mathematical Modeling of Industrial Life Cycles at Berlin Technical University. In his research, he combines methods from computer vision and applied mathematics. In particular, this applied to applications of generative learning to problems from mechanical engineering. Hanno Gottschalk studied mathematics and physics at the universities of Freiburg and Bochum and obtained his PhD in mathematical physics in 1999 at Bochum University. He thereafter went to Rome Universita La Sapienza for PostDoc studies as DAAD fellow and pursued with research at Bonn University, where in 2003 he habilitated in Mathematics. In 2007 he switched to industry as a Core Competency Owner for Probabilistic Design in gas turbine engineering with Siemens Energy. In 2011 he changed to the University of Wuppertal as associate professor for stochastics. In 2018 he founded the Interdisciplinary Center for Machine Learning and Data Analytics at U Wuppertal together with Anton Kummert and became the first elected speaker in 2020. Since April 2023 he joined TU Berlin.

Uwe Iben is a Chief Expert for Applied Mathematics at Bosch Research. His main research aeras includes AI methods for solving of ODEs and PDEs as surrogate models. He has joined the Robert Bosch GmbH in 1999 as a simulation engineer. He worked 15 years on the field of multi-phase flow and cavitation as a project leader and Chief expert. From 2016 till 2020 he became the head of Research and Technology Offices in Saint Petersburg/Russia. Dr. Iben studied mathematics at Moscow State University and Technical University of Dresden. After his PhD in the field of numerical mathematics, he worked as a PostDoc at Otto-von-Guericke University, Magdeburg. In 2003, he received his doctorate in the field of modeling of cavitating flow phenomena from Otto-von-Guericke University Magdeburg, Germany. He became an honorary professor at the University of Stuttgart in 2017 where he gives lectures on multi-phase flow phenomena.

Alexander Klawonn works as a probabilistic modeling specialist at Siemens Energy. His current work is focused on utilizing machine learning methods, such as physics-informed machine learning models and graph neural networks, for building fast surrogate models for different stationary and non-stationary physical systems. He studied mechanical/aerospace engineering at RWTH Aachen, mathematics at the University of Hagen and has a PhD in material science from TU Kaiserslautern.

Peter Maaß (Professor for Applied Mathematics at the Center for Industrial Mathematics at University of Bremen, ZeTeM). His main research areas include inverse problems, parameter identification, and since several years deep learning. Prof. Maass studied mathematics in Karlsruhe, Cambridge and Heidelberg and obtained his doctorate in 1988 from TU Berlin as well as his habilitation in mathematics from Saarland University in 1993. Peter Maass was a full professor at Potsdam University from 1993 -1999 before becoming director of ZeTeM. He spent several longterm research visits as guest professor or researcher at leading international universities including Paris, Berkeley, Boston and Cambridge.