Advanced Mathematical And Computational Tools In Metrology And Testing Xiii

Advanced Mathematical And Computational Tools In Metrology And Testing Xiii
Author :
Publisher : World Scientific
Total Pages : 366
Release :
ISBN-10 : 9789819800681
ISBN-13 : 9819800684
Rating : 4/5 (81 Downloads)

Book Synopsis Advanced Mathematical And Computational Tools In Metrology And Testing Xiii by : Franco Pavese

Download or read book Advanced Mathematical And Computational Tools In Metrology And Testing Xiii written by Franco Pavese and published by World Scientific. This book was released on 2024-11-15 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains original, refereed contributions by researchers from national metrology institutes, universities and laboratories across the world involved in metrology and testing. The volume has been produced by the International Measurement Confederation (IMEKO) Technical Committee 21, Mathematical Tools for Measurements, in association with IMEKO Technical Committee 6, Digitalisation, and the European Network for Mathematics and Statistics for Metrology, following presentations made at the Advanced Mathematical and Computational Tools in Metrology online conference, hosted by the Institute of Metrology of Bosnia and Herzegovina, Sarajevo, in September 2023. The volume covers the application of novel mathematical and statistical modelling techniques and data analysis and machine learning approaches to metrology and measurement science, uncertainty quantification, and knowledge representation and reasoning to enable the digitalization of metrological services.This volume is of interest to all researchers, data scientists, engineers and practitioners who need to characterize the capabilities of measurement systems, evaluate measurement data, quantify uncertainties and make inferences and decisions based on models and data. It will also be of interest to those working in the quality infrastructure concerned with the reliability, transparency, trustworthiness and reproducibility of data, data analytics, machine learning and AI, in engineering, physical, environmental and the life sciences.


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