  Surrogate modeling is a viable solution for applications involving repetitive
evaluations of expensive computational fluid dynamics models, such as
uncertainty quantification and inverse problems. This study proposes a
multi-layer perceptron (MLP) based machine-learning surrogate for canopy flow
statistics accommodating any approaching mean-wind angle. The training and
testing of the surrogate model is based on results from large-eddy simulations
of open-channel flow over and within surface-mounted cubes under neutral
ambient stratification. The training dataset comprises flow statistics from
various approaching mean-wind angles, and the surrogate is asked to "connect
between the dots," i.e., to predict flow statistics for unseen values of the
approaching mean-wind angle. The MLP performance is compared against a more
traditional spline-based interpolation approach for a range of training data.
In terms of relative mean absolute errors on individual flow statistics, the
proposed MLP surrogate consistently outperforms the spline interpolation,
especially when the number of training samples is reduced. The MLP model
accurately captures mean profiles and three-dimensional flow variability,
offering robust predictions, even when trained with as few as four approaching
wind angles. The model is $10^4 \times$ faster than large-eddy simulations,
thus proving effective for multi-query tasks in the context of urban canopy
flow modeling.
