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| MS06 - Reduced Order Modelling Invited Session organized by Trond Kvamsdal | MoA02I Room: Room1 Chair: Trond Kvamsdal |
Machine learning powered nonlinear manifold reduced order model Y. Kim, Y. Choi, D. Widemann and T. Zohdi Abstract |
Machine learning for nonlinear model order reduction. T. Daniel, F. Casenave, N. Akkari and D. Ryckelynck Abstract |
From linear mappings to deep learning for model order reduction of numerical simulations of industrial interest R. Bravo, C. Roig, R. Rossi and J Hernandez Abstract |
Data-driven reduced order modeling for CFD stochastic time-dependent problems A. Abdedou and A. Soulaïmani Abstract |
A hybrid POD-Galerkin approach for turbulent flow problems V. Tsiolakis, T. Kvamsdal, A. Rasheed, E. Fonn and H. Van Brummelen |
