ECCOMAS 2022

6/6/22    11:00 - 13:00
Machine Learning and Data-Driven Approaches for Aerodynamic Analysis and Uncertainty Quantification  
Minisymposium organized by Esther Andrés
Room: Spitsbergen
MS86A
Chair: Esther Andrés
Data-Driven Reduced Order Modeling for Aerodynamic Flow Predictions
Derrick A. Hines Chaves and Philipp Bekemeyer

Comparison of uncertainty quantification methods for mathematical and mechanical problems in intermediate dimensions
Jacques Peter and Quentin Bennehard

A comparison of machine learning methods for pressure coefficient prediction of an aeronautical configuration
Alejandro Gorgues, Rodrigo Castellanos, Jaime Bowen and Esther Andrés

Reynolds stress correction by machine learning methods with physical constraints
Thomas Philibert, Andrea Ferrero, Angelo Iollo and Francesco Larocca

MPI-Parallel Machine Learning Algorithms for the Analysis of High-Speed Video Data
Alexander Ruettgers and Anna Petrarolo

Neural network prediction of the flow field in a periodic domain with hyper-neural network parametrization
Ondřej Bublík, Václav Heidler, Aleš Pecka and Jan Vimmr