NeurIPS 2024 ML4CFD Competition
Vancouver, Canada - December 14, 2024
Invited Talk
Title: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
Abstract: We present MARIO (Multiscale Aerodynamic Resolution Invariant Operator), a conditional neural field approach tailored for aerodynamic predictions. MARIO employs multiple input Fourier feature embeddings at different scales to optimize the reconstruction accuracy across the different frequency components, especially for multi-output predictions. The architecture is conditioned through a hypernetwork and FiLM modulation on the free-stream conditions and airfoil geometry, parameterized through thickness and camber distributions. We enhance the traditional coordinate and signed distance function inputs with a continuous normal vector field on and off the airfoil surface aiming to provide a translation invariant frame of reference, augmenting the implicit distance information…