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Physical based simulations can be very time and computationally demandingtasks. One way of accelerating these processes is by making use of data-drivensurrogate models that learn from existing simulations. Ensembling methods areparticularly relevant in this domain as their smoothness properties coincidewith the smoothness of physical phenomena. The drawback is that they can remaincostly. This research project focused on studying Packed-Ensembles thatgeneralize Deep Ensembles but remain faster to train. Several models have beentrained and compared in terms of multiple important metrics. PE(8,4,1) has beenidentified as the clear winner in this particular task, beating down its DeepEnsemble conterpart while accelerating the training time by 25%.

Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround Airfoil Geometries