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Data-efficient operator learning for solving high Mach number fluid flow problems
We consider the problem of using SciML to predict solutions of high Mach fluid flows over irregular geometries.
Ensemble models outperform single model uncertainties and predictions for operator-learning of hypersonic flows
However, high-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space.
Neural Basis Functions for Accelerating Solutions to High Mach Euler Equations
We propose an approach to solving partial differential equations (PDEs) using a set of neural networks which we call Neural Basis Functions (NBF).
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