Projektbeschreibung
Neue CAD-Ingenieurgeneration in der Ausbildung
Die jüngsten Fortschritte in der Mathematik, in der computerunterstützten Entwicklung (CAD) und im maschinellen Lernen haben dafür gesorgt, dass zahlreiche Aufgaben im Zusammenhang mit Multimedia und 2D-Daten gelöst wurden. Jedoch liegt die Automatisierung der dreidimensionalen Verarbeitung und Analyse von Geometrien stark zurück. Die CAD-Branche braucht dringend fortgeschrittene Verfahren und Werkzeuge, um neuen Herausforderungen begegnen zu können. Das EU-finanzierte Projekt GRAPES zielt auf eine Schließung dieser Lücke durch ein multidisziplinäres Konsortium ab, das sich aus führenden Kräften der jeweiligen Bereiche zusammensetzt. Das Projekt arbeitet daran, eine neue europäische Wissenschaftler- und Ingenieurgeneration auszubilden, um Anwendungen im Bereich Simulation und Fertigung, Hydrodynamik und Schiffsbau, Fertigung und 3D-Druck zu entwickeln.
Ziel
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.
Wissenschaftliches Gebiet
- natural sciencescomputer and information sciencessoftware
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesmathematicspure mathematicsgeometry
- engineering and technologymechanical engineeringmanufacturing engineeringadditive manufacturing
- natural sciencesmathematicsapplied mathematicsnumerical analysis
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
MSCA-ITN - Marie Skłodowska-Curie Innovative Training Networks (ITN)Koordinator
151 25 Maroussi
Griechenland