Current projects

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KIWi - Hybrid Parameter Identification with Invertible Networks

PI: Tobias Kluth

In the near future, many wind turbines (WTs) will reach the end of their design life, typically 20 years. In the joint project KIWi, data- and model-based methods for model correction and for parameter identification in inexact models are used to enable a more accurate determination of the estimation of the load in wind turbines and thus to extend their possible lifetime.


TorchPhysics - A deep learning library for differential equations

Differential equations have to be solved in a wide variety of applications, usually by means of numerical simulations. Classical methods such as the Finite Element Method have shown to be succesful in various problems. However, some differential equations are very difficult to solve with classical methods, for example due to nonlinearities or their multi-scale character. Therefore, the aim of this project is to create a software package that combines various deep learning approaches for usage in various applications and further research. The project is developed in cooperation with the Robert Bosch GmbH.

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π³ - Parameter Identification – Analysis, Algorithms, Applications

Duration: 01.10.2016 - 30.09.2025
PI: Peter Maaß

In the Research Training Group π3 Parameter Identification - Analysis, Algorithms, Implementations, PhD students at the interface of Applied Mathematics and Scientific Computing focus on parameter identification issues, which are essentially modeled by minimizing appropriate objective functionals.

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Inverse problems - Theories, methods and implementations

Duration: 01.01.2021 - 31.12.2023
PI: Peter Maaß

Research on inverse problems has been an important, active field in applied mathematics for decades with a strong influence on many disciplines. In this German-Chinese mobility program, knowledge and ideas are exchanged, thus laying the foundation for long-term collaboration.

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Design-KIT - Artificial intelligence in mechanical component development

Duration: 01.10.2020 - 31.03.2022
PI: Peter Maaß

In the Design-KIT project, methods of artificial intelligence and machine learning are scientifically investigated for the design of components for launch vehicles and their usefulness for the corresponding industrial application is evaluated.

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KIDOHE - AI-supported documentation for midwives

Duration: 01.05.2020 - 28.02.2022
PI: Iwona Piotrowska-Kurczewski

KIDOHE aims to improve the stress and recourse situation of midwives by means of an innovative, intelligent, decision-support system. This system will represent both scientifically based expertise and experiential knowledge of midwives in networks (e.g. semantic networks, Bayesian networks or neural networks).

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D-MPI - Dynamic Inverse Problems in Magnetic Particle Imaging

Duration: 01.05.2020 - 30.04.2023
PI: Tobias Kluth

Magnetic Particle Imaging (MPI) is an imaging technique with promising medical applications based on the behavior of superparamagnetic iron oxide nanoparticles. In D-MPI, the dynamic aspects of concentration dynamics, magnetic field dynamics, and particle magnetization dynamics are studied to facilitate modeling and reconstruction of the data.


DELETO - Machine learning in correlative MR and high-throughput NanoCT.

Duratio: 01.04.2020 - 31.03.2023
PI: Tobias Kluth

In DELETO, Deep Learning methods for solving inverse problems will be decisively developed to make more accurate and efficient the reconstruction methods based on Structural Priors and Motion Correction in the field of correlative MR and high-throughput NanoCT, which are computationally expensive due to the large amount of data. The goal is to integrate these methods into next-generation devices.


HYDAMO - Hybrid data-driven and model-based simulation of complex flow problems in the automotive industry

Duration: 01.04.2020 - 31.03.2023
PI: Peter Maaß

On complex materials such as sand, mud or snow, vehicle stability is not always a given: Collisions or vehicle rollover may be unavoidable. The goal of HYDMAO is to integrate data-driven and model-based approaches into an overall solution based on a continuum mechanics problem from the vehicle industry that has been insufficiently understood to date. This is intended to decisively improve the computer-aided mapping of the associated process.


MALDISTAR - Study on quality assessment, standardization and reproducibility of MALDI imaging mass spectrometry data

Duration: 01.07.2019 - 30.06.2022
PI: Peter Maaß

MALDI imaging represents an established method for the spatial study of biomolecules. However, despite many advantages, it is becoming increasingly clear that the data are subject to high variability. For this reason, quality assessment tools and new calibration and cross-normalization methods are being developed in MALDISTAR.


ROMSOC - Reduced Order Modelling, Simulation and Optimization of Coupled Systems

Duration: 01.11.2017 - 31.08.2022
PI: Peter Maaß

The scientific goal of the PhD program is to develop the mathematical foundations and methods in the increasingly virtual development of industrial products and processes, focusing on coupling methods, model reduction techniques, and optimization methods.