MATISSE addresses the growing need for reliable methods to design, validate, and deploy Digital Twins (DTs) in complex industrial systems. Current DT solutions are fragmented, lack interoperability, and demand high manual effort for verification and validation across the system life cycle. The project responds to this gap by developing a model-based engineering framework that supports continuous design, testing, and integration of DTs in safety-critical and resource-intensive sectors.
The project aims to deliver:
1) A structured method for requirements-driven DT engineering.
2) A conceptual and technical architecture enabling traceability from use case needs to DT solutions.
3) Services for automated testing, monitoring, and prediction of system qualities.
4) Integrated demonstrators showing measurable efficiency and reliability gains across real industrial cases.
The expected impact concerns higher trust in DT adoption, reduced development cost, earlier fault detection, and stronger alignment with European strategies on digital transformation, cyber-physical resilience, and green industry. The first results already place the project in dialogue with policy agendas on trustworthy AI, standardisation of industrial data spaces, and strategic autonomy in microelectronics.
Social sciences and humanities contribute through methods for eliciting stakeholder requirements, ethical analysis of data governance, and studies of organisational readiness for DT uptake.