INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations
This addresses the computational burden of numerical simulations in industrial design, though it appears incremental as it builds on existing neural field methods.
The paper tackles the problem of creating efficient surrogate models for complex physical simulations by proposing INFINITY, a deep learning model using implicit neural representations to encode geometry and physical fields, achieving state-of-the-art performance on the AirfRANS dataset for airfoil design optimization.
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.