{"id":1826,"date":"2026-01-16T14:50:20","date_gmt":"2026-01-16T12:50:20","guid":{"rendered":"https:\/\/itml.gr\/?p=1826"},"modified":"2026-01-16T14:50:21","modified_gmt":"2026-01-16T12:50:21","slug":"turing","status":"publish","type":"post","link":"https:\/\/itml.gr\/rnd-projects\/turing\/","title":{"rendered":"TURING"},"content":{"rendered":"\n
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TURING (Trustworthy Unified Robust Intelligent Generative Systems) is a Horizon Europe project advancing the development of robust, trustworthy and efficient AI models for simulating complex physical systems. Across many scientific and engineering domains, high-fidelity numerical simulations remain computationally demanding and costly, limiting their scalability and practical deployment. TURING addresses this challenge by developing generative, multimodal AI foundation models capable of learning the governing dynamics of physical systems and delivering accurate predictions with significantly reduced computational effort.<\/p>\n\n\n\n

The project adopts a progressive and robustness-driven approach to AI model development. Foundation models are pre-trained on diverse datasets representing dynamic physical systems with shared characteristics and are gradually extended to capture increasing levels of physical complexity as robustness is validated. These models are subsequently fine-tuned for specific tasks, forming the TURING models, which are optimized for application in priority domains such as nuclear energy and safety, particle physics, and meteorology and climate modelling. Through this approach, TURING aims to overcome key limitations of current AI systems, including limited generalization, sensitivity to bias and reduced reliability when applied to unseen conditions. Trust, transparency and responsible AI are central to TURING\u2019s vision. The project integrates ethical, legal and social considerations throughout the AI lifecycle, with particular attention to explainability, robustness and alignment with European values and regulatory frameworks, including the EU AI Act. In parallel, TURING follows open science principles, ensuring that its methods, models and results are accessible, reproducible and reusable by the wider research and innovation community. To support adoption and long-term impact, TURING delivers the TURING Framework, a comprehensive set of MLOps tools and software components, complemented by a web-based interface with conversational capabilities. This framework enables researchers, developers and end users to access, adapt and deploy TURING models efficiently, fostering uptake across scientific and industrial contexts. All core outputs\u2014including models, benchmarks, datasets and tools\u2014will be publicly released, supporting collaboration and innovation beyond the project\u2019s duration.<\/p>\n\n\n\n

ITML contributes to TURING both as a technology provider and as leader of activities related to outreach, impact and social inclusion. Within this role, ITML supports the dissemination and communication of project results, contributes to impact assessment and community building across application domains, and helps ensure that TURING\u2019s outcomes are accessible, inclusive and aligned with European policy and societal priorities. TURING is implemented by a multidisciplinary international consortium bringing together expertise from AI, physics, engineering and social sciences across Europe, with additional partners in Canada, India and Switzerland. By combining cutting-edge AI research with a strong focus on robustness, openness and societal responsibility, TURING aims to establish a new generation of AI models that can be confidently applied to critical scientific and engineering challenges.<\/p>\n","protected":false},"excerpt":{"rendered":"

TURING builds robust AI foundation models that learn physics itself, cutting simulation cost while delivering trustworthy predictions for nuclear energy, particle physics, and meteorology.<\/p>\n","protected":false},"author":6,"featured_media":1827,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":true,"footnotes":""},"categories":[3],"tags":[],"keywords":[151,339,364,125,363,84,362,204,361,360,359],"class_list":["post-1826","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-rnd-projects","keywords-ai-powered-applications","keywords-cyber-and-civil-security","keywords-explainable-ai-xai","keywords-federated-learning","keywords-generative-foundation-models","keywords-machine-learning","keywords-model-pre-training-and-fine-tuning-techniques","keywords-multi-agent-systems","keywords-physics-informed-ai-models","keywords-signal-processing","keywords-statistical-data-processing"],"acf":[],"yoast_head":"\nTURING - 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