Uncertainty quantification by ensemble learning is explored in terms of an application known from the field of computational optical form measurements. The application requires solving a large-scale, nonlinear inverse problem. Ensemble learning is used to extend the scope of a recently developed deep learning approach for this problem in order to provide an uncertainty quantification of the solution to the inverse problem predicted by the deep learning method. 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 the basis of high-dimensional data in a real-world context.

根据计算光学形状测量领域已知的应用，探索了通过集成学习进行的不确定性量化。该应用程序需要解决大规模的非线性逆问题。集成学习用于扩展最近针对该问题开发的深度学习方法的范围，以便为深度学习方法预测的逆问题的解决方案提供不确定性量化。通过系统地插入分布外误差以及噪声数据，探索了所开发的不确定性量化的可靠性。结果令人鼓舞，所提出的应用程序举例说明了集成方法在现实世界环境中基于高维数据做出可靠预测的能力。