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REinforcement TWInning SysTems: from collaborative digital twins to model-based reinforcement learning

Objective

"The concept of digital twins promises to revolutionize engineering by offering new avenues for optimization, control, and predictive maintenance. Digital twins seek to virtually replicate systems using models that continuously ""learn"" from data, automatizing data collection, validation, and refinement and becoming ""self-learning"" models. However, this concept is not yet established in engineering and requires significant developments in integrating machine learning with traditional ""domain-specific"" knowledge. The Re-Twist project tackles this challenge with two objectives. The first objective is to develop a new framework that puts fundamental principles at the core of digital twinning and combines the training of a digital twin with the training of a controlling agent in ways that allow one to learn from the other. The agent learns by trial and error, as in reinforcement learning, while interacting with the system and using the digital twin as a playground. I call this novel framework Reinforcement Twinning (RT). The second objective is to develop RT on lab-scale prototypes of systems at the centre of global challenges. These are the optimal operation of wind turbines, drone propellers, sloshing tanks, and cryogenic liquid storage. Wind turbines drive the fastest-growing renewable energy sector; drones have the potential to revolutionize monitoring, inspection, rescue missions, swift delivery of medical supplies and more. The optimal management of cryogenic tanks, controlling the dynamics of sloshing and the thermodynamics of boil-off, will be essential to the economic viability of green fuels such as liquid hydrogen. This project is ""high risk"" because it endeavours to establish a new discipline at the intersection of machine learning and energy engineering. It promises ""high gains"" by aiming to experimentally validate twinning systems that could significantly impact society."

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

Multi-annual funding programmes that define the EU’s priorities for research and innovation.

Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2024-STG

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Host institution

VON KARMAN INSTITUTE FOR FLUID DYNAMICS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 771 250,00
Address
Waterloose Steenweg, 72
1640 Sint-Genesius-Rode
Belgium

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SME

The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.

Yes
Region
Vlaams Gewest Prov. Vlaams-Brabant Arr. Halle-Vilvoorde
Activity type
Research Organisations
Links
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 771 250,00

Beneficiaries (2)

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