Neural Basis Functions for Accelerating Solutions to high Mach Euler Equations

David Witman, Alexander New, Hicham Alkandry, Honest Mrema

Keywords: Machine Learning, Scientific Machine Learning, Neural Networks, Partial Differential Equations, Euler Equations

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.

Track: Original Research Track

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/neural-basis-functions-for-accelerating/code)