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learninG, pRocessing, And oPtimising shapES

Descripción del proyecto

Formar a la nueva generación de ingenieros CAD

Los avances recientes en matemáticas, diseño asistido por ordenador (CAD) y aprendizaje automático han ayudado a resolver muchas tareas relacionadas con datos multimedia y bidimensionales. Sin embargo, la automatización del procesamiento y análisis de geometrías tridimensionales va muy rezagada. El sector CAD necesita urgentemente métodos y herramientas avanzadas para hacer frente a nuevos retos. El objetivo del proyecto financiado con fondos europeos GRAPES es cerrar esta brecha a través de un consorcio multidisciplinar formado por líderes en sus respectivos campos. El proyecto trabaja para formar a una nueva generación de científicos e ingenieros europeos para crear aplicaciones que incluirán simulación y fabricación, hidrodinámica y diseño marino, fabricación e impresión tridimensional.

Objetivo

GRAPES aims at considerably advancing the state of the art in Mathematics, Computer-Aided Design, and Machine Learning in order to promote game changing approaches for generating, optimising, and learning 3D shapes, along with a multisectoral training for young researchers. Recent advances in the above domains have solved numerous tasks concerning multimedia and 2D data. However, automation of 3D geometry processing and analysis lags severely behind, despite their importance in science, technology and everyday life, and the well-understood underlying mathematical principles. The CAD industry, although well established for more than 20 years, urgently requires advanced methods and tools for addressing new challenges.

The scientific goal of GRAPES is to bridge this gap based on a multidisciplinary consortium composed of leaders in their respective fields. Top-notch research is also instrumental in forming the new generation of European scientists and engineers. Their disciplines span the spectrum from Computational Mathematics, Numerical Analysis, and Algorithm Design, up to Geometric Modelling, Shape Optimisation, and Deep Learning. This allows the 15 PhD candidates to follow either a theoretical or an applied track and to gain knowledge from both research and innovation through a nexus of intersectoral secondments and Network-wide workshops.

Horizontally, our results lead to open-source, prototype implementations, software integrated into commercial libraries as well as open benchmark datasets. These are indispensable for dissemination and training but also to promote innovation and technology transfer. Innovation relies on the active participation of SMEs, either as a beneficiary hosting an ESR or as associate partners hosting secondments. Concrete applications include simulation and fabrication, hydrodynamics and marine design, manufacturing and 3D printing, retrieval and mining, reconstruction and visualisation, urban planning and autonomous driving.

Coordinador

ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS
Aportación neta de la UEn
€ 543 805,97
Dirección
ARTEMIDOS 6 KAI EPIDAVROU
151 25 Maroussi
Grecia

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Región
Αττική Aττική Βόρειος Τομέας Αθηνών
Tipo de actividad
Research Organisations
Enlaces
Coste total
€ 543 805,97

Participantes (10)