Summary of Data-driven Modeling in Metrology — a Short Introduction, Current Developments and Future Perspectives, by Linda-sophie Schneider et al.
Data-driven Modeling in Metrology – A Short Introduction, Current Developments and Future Perspectives
by Linda-Sophie Schneider, Patrick Krauss, Nadine Schiering, Christopher Syben, Richard Schielein, Andreas Maier
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel paper on mathematical models in metrology showcases the pivotal role these models play in deriving measurement results, calculating uncertainties, and understanding the measurement process. The authors highlight how classic analytical models built on physical principles are being replaced by data-driven methodologies, particularly in complex sensor networks where real-world context is rapidly changing. This shift enables more accurate predictions and conclusions about the measurement system itself. The paper demonstrates various applications of data-driven modeling, showcasing its potential to revolutionize metrology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study explores how mathematical models help scientists calculate measurements and understand uncertainty. In the past, these models were based on basic physical principles. But with advancements in technology, a new approach called “data-driven” is gaining popularity. This method uses data from many sensors to make predictions and conclusions about what’s being measured. The paper shows how this new approach has already been used in real-world situations. |
