  Machine learning methods for the construction of data-driven reduced order
model models are used in an increasing variety of engineering domains,
especially as a supplement to expensive computational fluid dynamics for design
problems. An important check on the reliability of surrogate models is
Uncertainty Quantification (UQ), a self assessed estimate of the model error.
Accurate UQ allows for cost savings by reducing both the required size of
training data sets and the required safety factors, while poor UQ prevents
users from confidently relying on model predictions. We examine several machine
learning techniques, including both Gaussian processes and a family
UQ-augmented neural networks: Ensemble neural networks (ENN), Bayesian neural
networks (BNN), Dropout neural networks (D-NN), and Gaussian neural networks
(G-NN). We evaluate UQ accuracy (distinct from model accuracy) using two
metrics: the distribution of normalized residuals on validation data, and the
distribution of estimated uncertainties. We apply these metrics to two model
data sets, representative of complex dynamical systems: an ocean engineering
problem in which a ship traverses irregular wave episodes, and a dispersive
wave turbulence system with extreme events, the Majda-McLaughlin-Tabak model.
We present conclusions concerning model architecture and hyperparameter tuning.
