Deep learning and industrial applications

A declared goal of the group Industrial Mathematics and the Center for Industrial Mathematics is the transfer of modern mathematical methods to industry and science. Of particular importance are cooperation projects with companies in order to make them usable for concrete applications. A major goal is to make modern mathematical methods for problem solving accessible to small- and medium-sized companies (SMEs) as well.

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

The cooperating companies include: Airbus SAS, ArcelorMittal Bremen GmbH, ArianeGroup GmbH, atacama blooms GmbH & Ko. KG, Bruker Daltonik GmbH, ELISE GmbH, Siemens AG and SIKORA AG.

Prof. Dr. Dr. h.c. Peter Maaß

Leader of the WG Industrial Mathematics

Director of the ZeTeM

Team

Dr. Sören Dittmer

Deep Learning and Inverse Problems

Student assistants

Julius Arkenberg

Deep Learning and Industrial Applications

Saurabh Band

Deep Learning and Industrial Applications

Marvin Walther

Deep Learning and Industrial Applications

Projekte

AGENS - Analytic-generative networks for system identification

BMBF-Projekt
Duration: 01.04.2020 - 31.03.2023
PI: Peter Maaß

The prediction of the energy demand of individual actors based on time series is characterized by a huge amount of data. The goal of AGENS is the development and analysis of dynamic neural networks with a focus on energy forecasting. To enable a robust forecast per actor, an improvement of the data quality for each individual consumer is necessary.

Design-KIT - Artificial intelligence in mechanical component development

BMBF-Projekt
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.

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

BMBF-Projekt
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.

KIDOHE - AI-supported documentation for midwives

BAB-Projekt
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).

Updated by: Web Team AG Technomathematik

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