SOURCE-LINKED INTELLIGENCE
Trusted AI-Generated Digital Twins for Research InfraStructurEs
. Each case will demonstrate predictive maintenance, treatment optimisation, or real-time diagnostic improvements, advancing TRLs from 4-5 to 6-7. TwinRISE advances the state of the art by combining: generative AI pipelines for automated twin assembly; federated infrastructures enabling collaborative training without raw data exchange; explainability and reliability frameworks aligned with the EU AI Act; and agentic, human-in-the-loop interfaces for transparent decision support. Expected impacts include: up to 30% downtime and commissioning reduction in accelerator and medical systems; 15–20% energy savings via optimised HPC training and control; enhanced patient outcomes through safer and faster planning; and broad uptake of FAIR-compliant models and workflows. Oncology, Particle accelerators, Dosimetry, AI digital twins, federated learning, explainable AI, trustworthiness, FAIR data, HPC, EuroHPC, proton therapy, MRI, energy efficiency, interoperability
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 9999912.72
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T05:31:32.981Z. This is not the publication date.