ASIMOV Cookbook

ASIMOV Cookbook

good practices and lessons learned to create autonomous and self-optimizing CPSs

Good practices and lessons learned to create autonomous and self-optimizing CPSs

Good practices and lessons learned to create autonomous and self-optimizing CPSs

ASIMOV cookbook

Are you a strategist, R&D manager, systems architect or system engineer faced with questions on system optimization and considering AI?

In the ASIMOV project, we researched innovative technologies, combining AI and digital twinning, that can be used to create autonomous and self-optimizing CPSs (Cyber Physical Systems). In the project, we identified good practices and lessons learned to help with answering the question: "How to build complex high-tech CPSs that select their optimal settings autonomously within minimal time and with minimal external expertise?"

The ASIMOV cookbook gathers these good practices and lessons learned. It gives guidance on how to assess whether there is a viable business case, and how to get started. It also gives an overview of our experience in constructing an ASIMOV solution, focusing on the overall architecture, as well as the AI and digital twinning parts. Furthermore, it provides guidance on embedding the solution in the organization and on how to turn the initial solution into a mature and successful result.

Interested? Read the ASIMOV cookbook

Editors: Bram van der Sanden, Richard Doornbos, Jan van Doremalen

Contributors (alphabetically): AVL Deutschland GmbH, Consultants in Quantitative Methods CQM B.V., Eindhoven University of Technology, Thermo Fisher Scientific, German Aerospace Center (DLR), LiangDao GmbH, NorCom Information Technology GmbH & Co. KGaA, RA Consulting GmbH, The Netherlands Organisation for Applied Scientific Research (TNO) - Embedded Systems Innovation (ESI), Triangraphics GmbH

Read the ASIMOV cookbook
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