Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations

David Witman; Alexander New; Hicham Alkendry; Honest Mrema

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arXiv · 2208.01687

Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations

David Witman·Alexander New·Hicham Alkendry·Honest Mrema

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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.

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BibTeX RIS

David Witman, Alexander New, Hicham Alkendry, Honest Mrema. 2022-08-02. Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations. https://arxiv.org/abs/2208.01687

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