Cognitive Digital Twin

Cognitive Digital Twin

  • Duration:

This project aims to develop the next generation of the digital twin, which is self-learning and proactive. This means firstly, it learns from existing, real data to run like processes. Secondly, it can adapt to changes in the process through self-learning. And thirdly, it recognizes problems before they arise, so that all processes always work at approximately the optimum. To achieve this, the latest technologies from the fields of Big Data, Databases, IoT, Smart Sensors, Hybrid Modelling, Machine Learning and AI are used. The main objective is to support the process optimization of the European industry. And also to further promote technology in Europe.


SINTEF AS, Hydro Aluminium Deutschland GmbH, SHI FW Energia Oy, Sidenor Aceros Especiales Europa S.L., Elkem ASA, Saarstahl AG, Noksel Steel Pipe Company, DFKI, Fraunhofer Gesellschaft, University of Oulu, Cybernetica AS, Nissatech Innovation Centre, TEKNOPAR Industrial Automation, Scortex


Federal Ministry of Economics and Energy (BMWi)

Federal Ministry of Economics and Energy (BMWi)

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Contact Person
Prof. Dr.-Ing. Philipp Slusallek
Prof. Dr.-Ing. Philipp Slusallek

Publications about the project

Christian Schorr, Fei Chen, Tim Dahmen

In: Antonio Fernández-Caballero (editor). Applied Sciences 11 - Special Issue on Explainable AI 5 Pages 2199-2215 MDPI Basel 3/2021.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz