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Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)
Machine-learning paradigms such as imitation learning and reinforcement learning can generate highly performant agents in a variety of complex environments.
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.
Adaptive Neural Networks Using Residual Fitting
In contrast, methods that add capacity to neural networks as needed may provide similar results to architecture search and pruning, but do not require as much computation to find an appropriate network size.
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