{"id":1866,"date":"2026-10-27T08:39:38","date_gmt":"2026-10-27T07:39:38","guid":{"rendered":"https:\/\/www.i3a.uclm.es\/raap\/?page_id=1866"},"modified":"2026-10-27T13:42:47","modified_gmt":"2026-10-27T12:42:47","slug":"platgdia","status":"publish","type":"page","link":"https:\/\/www.i3a.uclm.es\/raap\/?page_id=1866","title":{"rendered":"PlatGDIA: Plataforma de creaci\u00f3n de Gemelos Digitales intensivos en IA"},"content":{"rendered":"

Digital twins are becoming a key technology for the digital transformation of industry. They create virtual representations of real-world systems that continuously incorporate data from sensors and operational environments, enabling simulation, prediction, optimization, and decision support. However, developing advanced digital twins that integrate artificial intelligence remains a complex and costly task, often requiring bespoke solutions, specialized expertise, and significant development effort. The PlatGDIA project addresses this challenge by establishing a common framework for the creation of AI-intensive Digital Twins.<\/p>\n

The project will develop DTMeth, an end-to-end methodology for the development and validation of digital twins. This methodology will define best practices, development processes, validation procedures, and lifecycle management strategies specifically tailored to digital twins with embedded AI capabilities. Building upon modern DevSecOps and MLSecOps principles, DTMeth will provide a structured approach that organizations can adopt to create reliable and secure digital twin solutions.<\/p>\n

A second key outcome is PlatGDIA, a software platform designed to support the implementation, deployment, monitoring, and validation of digital twins. The platform will integrate tools for data ingestion, AI model management, simulation, validation, and interoperability, reducing the technical barriers associated with digital twin development. By providing a shared technological foundation, PlatGDIA will enable faster and more efficient creation of digital twins across a wide range of industrial domains.<\/p>\n

To further accelerate development, the project will deliver DTBlocks, a reusable library of standard and interoperable software components. These building blocks will encapsulate common functionalities such as AI model management, predictive analytics, prescriptive optimization, IoT integration, data federation, communications, positioning services, and validation mechanisms. The reuse of DTBlocks will significantly reduce development effort while improving maintainability, scalability, and interoperability.<\/p>\n

The proposed methodology and platform will be validated through multiple demanding use cases, including digital twins for IoT sensor networks, industrial UAVs, autonomous vehicles, smart laboratories, manufacturing processes, water management systems, and industrial production lines. These demonstrators will showcase the flexibility of the approach and demonstrate how digital twins can enhance operational efficiency, predictive maintenance, decision support, sustainability, and resource optimization.<\/p>\n

Ultimately, PlatGDIA aims to establish the foundations for the standardization and widespread adoption of AI-intensive Digital Twins, strengthening the competitiveness of European industry while fostering innovation in sectors where advanced simulation, artificial intelligence, and data-driven decision making are becoming strategic assets.<\/p>\n

The role of the RAAP group in this project is leading the conceptualization and deployment of an urban air mobility (UAM) digital twin with unmanned traffic management (UTM). This contribution encompasses simulating end-user mission requests, conflict-free flight plans for those missions, and the integration of unmanned traffic within the controlled airspace framework. More information: https:\/\/mtp.global\/es\/innovacion\/proyectos-idi\/platgdia\/<\/a><\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

Digital twins are becoming a key technology for the digital transformation of industry. They create virtual representations of real-world systems that continuously incorporate data from sensors and operational environments, enabling simulation, prediction, optimization, and decision support. However, developing advanced digital twins that integrate artificial intelligence remains a complex and costly task, often requiring bespoke solutions, specialized expertise, and significant development effort. The PlatGDIA project addresses this challenge by establishing a common framework for the creation of AI-intensive Digital Twins. The project will develop DTMeth, an end-to-end methodology for the development…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":13,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1866","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/pages\/1866","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1866"}],"version-history":[{"count":5,"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/pages\/1866\/revisions"}],"predecessor-version":[{"id":1874,"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/pages\/1866\/revisions\/1874"}],"up":[{"embeddable":true,"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=\/wp\/v2\/pages\/13"}],"wp:attachment":[{"href":"https:\/\/www.i3a.uclm.es\/raap\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1866"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}