2026 - KOMSO Academy

Hanno Gottschalk  is an internationally leading expert for AI based generative modelling of technical processes. His research spans from theoretical investigations for stochastic AI models to real life applications such as modelling of turbulence. In this context he recently published a highly appreciated review on how generative models help to solve parameter identification problems. He is also the KOMSO representative for national AI initiatives. Moreover, he is highlighted by NVIDIA research as one of their main collaborators, e.g. he receives generous funding from the NVIDIA Academic Grant Program for Researcher.

Hanno Gottschalk received his PhD under the supervision of Sergio Albeverio (1999, Bochum) before spending PostDocs in Rome and Bonn. From 2011 – 2023 he was Professor for Stochastic at the Bergische Universität Wuppertal before joining the TU Berlin, where he holds the Werner-von-Siemens-Professorship for ‘Mathematical modelling of industrial lifecycles’.

Marius Zeinhofer is a postdoctoral fellow in Prof. Siddhartha Mishra’s research group at ETH Zurich. 

His research focuses on developing geometric optimization methods for scientific machine learning applications, including physics-informed neural networks (PINNs), neural quantum states, and neural operators. 

He is particularly recognized for his contributions to natural gradient methods for high-accuracy PINN training, as well as his numerical analysis of both PINNs and the deep Ritz method.

Julian Nürk is a PhD student at Bosch Research and Heidelberg University. His main research area is  scientific machine learning for multiscale problems in the context of uncertainty quantification and virtual validation. 

Working closely together with the research group of “Numerical Analysis and UQ” from Heidelberg University, led by Prof. Dr. Robert Scheichl, he focuses on enhancing advanced numerical multiscale methods with machine and operator learning.

During his Master’s he gained industry experience by various collaborations with BOSCH, applying math knowledge to real-world problems. He holds a Bachelor’s degree in Mathematics and a Master in Scientific Computing from Heidelberg University.

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.

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.

Conference Dinner,  November 30

Included in the conference fee for on-site attendees.