Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations

TL;DR

This paper introduces Neural Basis Functions, a neural network framework that accelerates solving high Mach Euler equations by providing reduced order solutions that improve CFD solver convergence.

Contribution

It presents a novel neural network approach combining POD and DeepONet techniques for efficient PDE solutions in high-speed aerodynamics.

Findings

NBF provides accurate reduced order solutions for high Mach Euler equations.

Using NBF as initial conditions accelerates CFD solver convergence.

The method demonstrates effectiveness in high-speed flow simulations.

Abstract

We propose an approach to solving partial differential equations (PDEs) using a set of neural networks which we call Neural Basis Functions (NBF). This NBF framework is a novel variation of the POD DeepONet operator learning approach where we regress a set of neural networks onto a reduced order Proper Orthogonal Decomposition (POD) basis. These networks are then used in combination with a branch network that ingests the parameters of the prescribed PDE to compute a reduced order approximation to the PDE. This approach is applied to the steady state Euler equations for high speed flow conditions (mach 10-30) where we consider the 2D flow around a cylinder which develops a shock condition. We then use the NBF predictions as initial conditions to a high fidelity Computational Fluid Dynamics (CFD) solver (CFD++) to show faster convergence. Lessons learned for training and implementing this algorithm will be presented as well.