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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…