3 papers
cs.LG2023
Data-efficient operator learning for solving high Mach number fluid flow problems
Noah Ford, Victor J. Leon, Honest Mrema +2
We consider the problem of using SciML to predict solutions of high Mach fluid flows over irregular geometries. In this setting, data is limited, and so it is desirable for models…
physics.flu-dyn2023
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows
Victor J. Leon, Noah Ford, Honest Mrema +2
High-fidelity computational simulations and physical experiments of hypersonic flows are resource intensive. Training scientific machine learning (SciML) models on limited high-fid…
cs.LG2022
Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations
David Witman, Alexander New, Hicham Alkendry +1
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 var…