The need to implement complex physics systems is critical across various scientific and engineering domains. However, traditional numerical models for simulating these systems are computationally expensive, requiring significant time, resources, and cost. Recent advancements in AI present a promising alternative, with AI models demonstrating the ability to capture the dynamics of complex physical systems. Despite these successes, AI models suffer from key limitations, including challenges with generalization, vulnerability to bias, ethical concerns, and accuracy, particularly when applied to unseen tasks or variable-range predictions. These limitations are collectively viewed as issues of robustness. The TURING project aims to address these shortcomings by developing robust AI-driven solutions. It integrates multidisciplinary advancements from Machine Learning, Computer Engineering, Physics, and SSH to pre-train generative, multimodal foundation models capable of capturing the physics of dynamic systems that share common properties. Starting with a cautious approach, the models will incorporate representations of increasingly complex physical systems as robustness is ensured. Once pre-trained, these foundation models will be fine-tuned for specific tasks, enhancing their domain-specific robustness. The tasks will target critical engineering and physics problems in nuclear energy, particle physics, and meteorology, which are of high priority for the EU. The task-specific and foundation models, collectively termed "TURING models", will be developed in collaboration with partners from India, Canada, and Switzerland. To maximize the impact of TURING models, the project will ensure compliance of its activities with regulations such as the EU AI Act and then publicly release those models, along with the TURING Framework (MLOps SW tools and web-based app with conversational capabilities), enabling developers and end users to leverage this technology for their applications.

Despite the tremendous socio-economic importance of Supply Chains (SCs), security officers and operators have still no easy and integrated way to protect their interconnected Critical Infrastructures (CIs) and cyber systems in the new digital era. CYRENE vision is to enhance the security, privacy, resilience, accountability and trustworthiness of SCs through the provision of a novel and dynamic Conformity Assessment Process (CAP) that evaluates the security and resilience of supply chain services, the interconnected IT infrastructures composing these services, and the individual devices that support the operations of the SCs. In order to meet its objectives, the proposed CAP is based on a collaborative, multi-level evidence-driven, Risk and Privacy Assessment approach that support, at different levels, the SCs security officers and operators to recognize, identify, model, and dynamically analyse advanced persistent threats and vulnerabilities as well as to handle daily cyber-security and privacy risks and data breaches. CYRENE will be validated in the scope of realistic scenarios/conditions comprising of real-life supply chain infrastructures and end-users. Furthermore, the project will ensure the active engagement of a large number of external stakeholders as a means of developing a wider ecosystem around the project’s results, which will set the basis for CYRENE large scale adoption and global impact.

The “Smart City” paradigm aims to support new forms of monitoring and managing of resources as well as to provide situational awareness in decision-making fulfilling the objective of servicing the citizen, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects. Considering the city as a complex and dynamic system involving different interconnected spatial, social, economic, and physical processes subject to temporal changes and continually modified by human actions. Big Data, fog, and edge computing technologies have significant potential in various scenarios considering each city individual tactical strategy. However, one critical aspect is to encapsulate the complexity of a city and support accurate, cross-scale and in-time predictions based on the ubiquitous spatio-temporal data of high-volume, high-velocity and of high-variety. To address this challenge, MARVEL delivers a disruptive Edge-to-Fog-to-Cloud ubiquitous computing framework that enables multi-modal perception and intelligence for audio-visual scene recognition, event detection in a smart city environment. MARVEL aims to collect, analyse and data mine multi-modal audio-visual data streams of a Smart City and help decision makers to improve the quality of life and services to the citizens without violating ethical and privacy limits in an AI-responsible manner. This is achieved via: (i) fusing large scale distributed multi-modal audio-visual data in real-time; (ii) achieving fast time-to-insights; (iii) supporting automated decision making at all levels of the E2F2C stack; and iv) delivering a personalized federated learning approach, where joint multi modal representations and models are co-designed and improved continuously through privacy aware sharing of personalized fog and edge models of all interested parties.