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

arXiv: https://arxiv.org/abs/2312.13403 · HTML: https://mlsanity.com/paper/2312.13403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions](https://mlsanity.com/paper/2212.07564/) (2022)

## Built on by (most cited first, 1 of 1)

- [Implicit Neural Representation For Accurate CFD Flow Field Prediction](https://mlsanity.com/paper/2408.06486/) (2024)

## Abstract (arXiv)

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%.