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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.16659 |
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| _version_ | 1866929397841264640 |
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| author | Schneider, Linda-Sophie Krauss, Patrick Schiering, Nadine Syben, Christopher Schielein, Richard Maier, Andreas |
| author_facet | Schneider, Linda-Sophie Krauss, Patrick Schiering, Nadine Syben, Christopher Schielein, Richard Maier, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16659 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives Schneider, Linda-Sophie Krauss, Patrick Schiering, Nadine Syben, Christopher Schielein, Richard Maier, Andreas Machine Learning Signal Processing 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. |
| title | Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2406.16659 |