  Uncertainty quantification by ensemble learning is explored in terms of an
application from computational optical form measurements. The application
requires to solve a large-scale, nonlinear inverse problem. Ensemble learning
is used to extend a recently developed deep learning approach for this
application in order to provide an uncertainty quantification of its predicted
solution to the inverse problem. By systematically inserting
out-of-distribution errors as well as noisy data the reliability of the
developed uncertainty quantification is explored. Results are encouraging and
the proposed application exemplifies the ability of ensemble methods to make
trustworthy predictions on high dimensional data in a real-world application.
