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Session Chair: Jörg Schröder, UDE Session Chair: Alexander Schwarz, University of Duisburg-Essen
Location:Auditorium Wolfsburg
Presentations
Simulations efficiency empowered by the use of model order reduction and artificial intelligence
Francisco Chinesta1, Elias Cueto2
1ENSAM, France; 2University of Zaragoza, Spain
Most of models encountered in applied physics can be nowadays numerically solved by using appropriate discretization techniques and adequate computational resources. However, sometimes, the predictions obtained from these physics-based simulations exhibit noticeable bias, and their solution is not compatible with real-time applications, compulsory in generative design or control of engineering systems.
In this work we propose the use of a hybrid paradigm, in which the alliance of physics-based and data-driven models, the last making use of artificial intelligence and machine learning technologies, enables improving the accuracy while ensuring real-time responses, enlarging the horizon of engineering.
