Predictions from science and engineering models depend on the values of the model’s input parameters. As the number of parameters increases, algorithmic parameter studies like optimization or uncertainty quantification require many more model evaluations. One way to combat this curse of dimensionality is to seek an alternative parameterization with fewer variables that produces comparable predictions. The active subspace is a low-dimensional linear subspace of the space of model inputs that captures the variability in the model’s predictions. We describe a method for checking if a model admits an exploitable active subspace, and we apply this method to a single-diode solar cell model. We find that the maximum power of the solar cell has a dominant one-dimensional active subspace in its space of five input parameters.
['Paul G. Constantine', 'Brian Zaharatos', 'Mark Campanelli']
Discovering an active subspace in a single-diode solar cell model