WG Industrial Mathematics

  • Welcome - The WG Industrial Mathematics of the ZeTeM introduces itself

    The WG Industrial Mathematics is composed of an international and interdisciplinary team from different disciplines of applied mathematics.

  • Autumn School: Deep Learning and Inverse Problems

    Autumn School Deep Learning and Inverse Problems

    November 04th - 08th 2019, University of Bremen

Prognose des Energiebedarfs

Our research

The Technomathematics group covers a wide range of research and applications from the fields of life sciences and engineering.

The focus of the mathematical research is on

  • Inverse problems
  • Mathematical image and signal processing
  • Deep Learning
  • Numerical Analysis
  • Parameter identification


WG Industrial Mathematics
Fachbereich 3

Prof. Dr. Dr. h.c Peter Maass

Bibliothekstraße 5
28359 Bremen

Phone: +49 421 218-63802 or -63800 (Secretariat)

Email: pmaassprotect me ?!uni-bremenprotect me ?!.de

Aktive Nervenzellen

Deep Learning

Our central research focus is Deep Learning and neural networks. In the context of diverse research projects, fundamental topics such as the connection of Deep Learning with Inverse Problems or invertible neural networks are investigated.

One highlight of these activities was, for example, the organization of the Autumn School on Deep Learning and Inverse Problems. Applications of the current research results include medical imaging, e.g. data-based reconstruction methods in computed tomography, or tumor classification based on MALDI imaging. This transfer of novel mathematical methods of Deep Learning into industrial applications is also supported by events like the Deep Learning Forum Bremen.

Research focus and teams

Digitale Pathologie

Deep Learning and Digital Pathology

The DigiPath team is working on various challenges in the field of digital pathology. These include, for example, the classification of tumor tissue or tumor types based on digital microscopy images and mass spectrometry imaging data.


Inverse Problems and Magnetic Particle Imaging

The MPI team is addressing the challenges that this technology poses for medical research. These include the detailed mathematical modeling of the measurement process. Furthermore, due to a currently sparse data situation, many of the most common data-driven methods are not directly applicable.


Deep Learning and Inverse Problems

The DLIP team conducts research on the combination of Deep Learning methods with classical approaches from the field of inverse problems. Particular emphasis is placed on the data-free Deep Image Prior method, the integration of physical model knowledge into the structure of artificial neural networks, and regularization techniques.

Schematisches Bild für Design-KIT

Deep Learning and Industrial Applications

In numerous industrial research projects - in close cooperation with experts in the respective fields of application - the entire problem-solving process is worked on, from modeling the initial problem to the mathematical analysis of the model to software development.



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Dr. Matthias Beckmann is Visiting Researcher at Imperial College London

Dr. Matthias Beckmann, research associate in the WG Industrial Mathematics, is Visiting Researcher at the Imperial College London in the Communications and Signal Processing Group of the Department of Electrical and Electronic Engineering until 01.07.2023.

Prof. Mohsen Tadi
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Prof. Mohsen Tadi visits ZeTeM

Prof. Mohsen Tadi, Associate Professor in the department of Engineering at Central Connecticut State University, is currently on a research stay at the ZeTeM.

AI industrial problems
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KoMSO Academy 2021

From 14-16 September, the first KoMSO Academy "Combining model- and data-based approaches for industrial problems", organised by the Centre for Industrial Mathematics (ZeTeM) and the Committee for Mathematical Modeling, Simulation, and Optimization (KoMSO), took place in Bremen in hybrid form.

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MarDATA in Antarctica

In the WG Industrial Mathematics, models for the automated analyses of earthquake events in Antarctica were developed based on machine learning methods.

Logo of the University of Bremen on a glass pane.
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Bremen Study Prize for Louisa Kinzel

Congratulations to Louisa for winning the Bremen Study Prize for her Master's thesis. Congratulations!

Logo Graduiertenkolleg
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Next funding period of the RTG has been approved!

We are pleased to announce that the DFG has approved another funding period (until 2025) for the π³ Research Training Group. A total of 25 Research Training Groups (10 of them new) will be funded.

KI Forschung am ZeTeM
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Third-party funding of €2 million for AI research in Bremen

The mathematicians at the Center for Technomathematics (ZeTeM) at the University of Bremen are on the trail of the secrets of Artificial Intelligence (AI). It is now widely known that deep learning and other AI processes achieve amazing results for a multitude of applications.

Digital Twins
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Digital Twins: Industrial and Mathematical Challenges

On May 7 and 8, 2019, the Challenge Workshop "Digital Twins" will take place at the Heidelberg Academy of Sciences. This event brings together experts from various industries and academia to discuss mathematical challenges in the field of digital twins.

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Review of the European doctoral program ROMSOC in Bremen

On November 26 and 27, 2018, the review of the EU-funded project ROMSOC - Reduced Order Modeling, Simulation and Optimization of Coupled systems took place in Bremen. In addition to the project partners, the project advisor of the European Commission was also a guest.

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