2021/03/01 by Lara Hoffmann, Hoffmann, Lara, Ines Fortmeier +3 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Advanced Measurement and Metrology Techniques #Advanced Optical Sensing Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Optical measurement and interference techniques #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2103.01259
23 pages, 20 figures
arxiv created 2021/03/01 · openalex publication_date 2021/03/01 · arxiv updated 2021/03/03 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.