The main objective of the MATISSE project is to create a framework incorporating methods and tools for the efficient and continuous engineering and validation of industrial systems that are supported by Digital Twins (DT), leveraging the advantages of model-based, data-driven, and cloud techniques to enable validation and verification services to improve productivity and quality significantly. MATISSE aims to establish an efficient model-based framework for continuously engineering federated DTs that share a unified system DT through aggregation mechanisms. This framework will enable MATISSE to offer DT-based services, including verification, prediction, and monitoring, thereby enhancing the overall engineering and federation of DTs. This effort heavily relies on utilizing models and model-based techniques, encompassing both general-purpose languages like UML/SysML and domain-specific languages (DSLs) alongside model transformations. These tools will address various aspects of the system engineering process, including design, modelling quality, testing, verification, and validation, among others. For instance, DSLs can be employed for defining business and engineering processes, describing the information needed for performing AI-based prescriptive, descriptive, and predictive analytics, and representing the actual systems with an appropriate level of detail that allows for, e.g., simulation and code generation. Model transformations offer automation for, e.g., manipulating artefacts, integrating the real-time data generated by the systems, and generating DTs, or parts of them, from models conforming to DSLs. As part of a more efficient engineering process, MATISSE will define a continuous validation strategy for DTs to reach a higher degree of (by-design) resilience for these systems. Moreover, MATISSE will employ data-driven techniques relying on historical and real-time data for defining a set of AI-based DT-services, part of the MATISSE infrastructure, supporting prediction, testing, and monitoring.
The participation of ISEP is led by the SoftCPS laboratory, working in approaches for integration of real-time data, to provide the necessary real-time ingestion of data from the cyber-physical system into the DT, enabling a more accurate representation of state and behaviour, and in integrating into the DT runtime analysis methods of non-functional properties such as performance, security and reliability. SoftCPS leads the Portuguese consortium in MATISSE and is also in the Project Management Team advisors.
Funded within the Chips Joint Undertaking (formerly KDT JU) from the European Union’s Horizon Europe Programme and the National Authorities, under grant agreement 101140216.
Budget: 18 M€ (Total), 412.5 K€ (ISEP)
Period: September 2024 to August 2027
SoftCPS Responsible: Luis Miguel Pinho