3 papers
cs.LG2024
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)
Noah Ford, Ryan W. Gardner, Austin Juhl +1
Machine-learning paradigms such as imitation learning and reinforcement learning can generate highly performant agents in a variety of complex environments. However, commonly used…
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…