Data-driven modeling in metrology - A short introduction, current developments and future perspectives

Schneider LS, Krauß P, Schiering N, Syben C, Schielein R, Maier A (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

Technisches Messen Oldenbourg Verlag

Book Volume: 91

Pages Range: 480-503

Journal Issue: 9

DOI: 10.1515/teme-2024-0004

Abstract

Mathematical models are vital to the field of metrology, playing a key role in the derivation of measurement results and the calculation of uncertainties from measurement data, informed by an understanding of the measurement process. These models generally represent the correlation between the quantity being measured and all other pertinent quantities. Such relationships are used to construct measurement systems that can interpret measurement data to generate conclusions and predictions about the measurement system itself. Classic models are typically analytical, built on fundamental physical principles. However, the rise of digital technology, expansive sensor networks, and high-performance computing hardware have led to a growing shift towards data-driven methodologies. This trend is especially prominent when dealing with large, intricate networked sensor systems in situations where there is limited expert understanding of the frequently changing real-world contexts. Here, we demonstrate the variety of opportunities that data-driven modeling presents, and how they have been already implemented in various real-world applications.

Authors with CRIS profile

Linda-Sophie Schneider Lehrstuhl für Informatik 5 (Mustererkennung) Patrick Krauß Department of Otorhinolaryngology – Head and Neck Surgery Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Additional Organisation(s)

Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Zentrum für Messen und Kalibrieren & ANALYTIK GmbH DE Germany (DE) Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Schneider, L.-S., Krauß, P., Schiering, N., Syben, C., Schielein, R., & Maier, A. (2024). Data-driven modeling in metrology - A short introduction, current developments and future perspectives. Technisches Messen, 91(9), 480-503. https://doi.org/10.1515/teme-2024-0004

MLA:

Schneider, Linda-Sophie, et al. "Data-driven modeling in metrology - A short introduction, current developments and future perspectives." Technisches Messen 91.9 (2024): 480-503.

BibTeX: Download