INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations

TL;DR

INFINITY is a deep learning framework using implicit neural representations to efficiently model Reynolds-Averaged Navier-Stokes equations, enabling accurate physical field inference and aiding design optimization tasks.

Contribution

The paper introduces INFINITY, a novel neural field modeling approach that encodes geometric and physical information for RANS equations, achieving state-of-the-art accuracy.

Findings

Achieves high accuracy in physical field inference on AirfRANS dataset

Successfully predicts drag and lift coefficients consistent with physical laws

Demonstrates potential for design exploration and shape optimization

Abstract

For numerical design, the development of efficient and accurate surrogate models is paramount. They allow us to approximate complex physical phenomena, thereby reducing the computational burden of direct numerical simulations. We propose INFINITY, a deep learning model that utilizes implicit neural representations (INRs) to address this challenge. Our framework encodes geometric information and physical fields into compact representations and learns a mapping between them to infer the physical fields. We use an airfoil design optimization problem as an example task and we evaluate our approach on the challenging AirfRANS dataset, which closely resembles real-world industrial use-cases. The experimental results demonstrate that our framework achieves state-of-the-art performance by accurately inferring physical fields throughout the volume and surface. Additionally we demonstrate its applicability in contexts such as design exploration and shape optimization: our model can correctly predict drag and lift coefficients while adhering to the equations.