Packed-Ensemble Surrogate Models for Fluid Flow Estimation Arround
  Airfoil Geometries
Anthony Kalaydjian Anton Balykov Alexi Semiz Adrien Chan-Hon-Tong
Physical based simulations can be very time and computationally demanding
tasks. One way of accelerating these processes is by making use of data-driven
surrogate models that learn from existing simulations. Ensembling methods are
particularly relevant in this domain as their smoothness properties coincide
with the smoothness of physical phenomena. The drawback is that they can remain
costly. This research project focused on studying Packed-Ensembles that
generalize Deep Ensembles but remain faster to train. Several models have been
trained and compared in terms of multiple important metrics. PE(8,4,1) has been
identified as the clear winner in this particular task, beating down its Deep
Ensemble conterpart while accelerating the training time by 25%.