Mathematical and Statistical Methods for Metrology

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Programme

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Invited speakers

• Lorenzo Tamellini, Institute of Applied Mathematics and Information Technologies “Enrico Magenes” (CNR-IMATI Pavia, Italy)
  A multi-fidelity method for uncertainty quantification in engineering problems - Abstract >
• Botond Tibor Szabó, Bocconi University, Department of Decision Sciences (Milano, Italy)
  On the theoretical understanding of Bayesian methods in complex models - Abstract >

Programme at a glance

The programme will include organized sessions specifically dedicated to the output and the research topics of the following EMPIR and EPM Projects:

• MATHMET Project “Support for a European Metrology Network for mathematics and statistics”.
  The session will be dedicated to the presentation of two main deliverables of the Joint Network Project that supported the establishment of the European Metrology Network for Mathematics and Statistics (EMN Mathmet). In particular, the Mathmet Strategic Research Agenda (SRA) and the Quality Assessement Tools (QAT) for software, data and guidelines will be presented. The SRA collected emerging research topics such as machine learning and virtual metrology and linked them to traditional areas such as uncertainty quantification, data analysis, and modelling; the QAT provided a framework to ensure that research outputs in the forms of data, software and guidelines are fit-for-purpose and quality-assured.
• RaCHy Project “Radiotherapy coupled with hyperthermia”.
  The session will provide an overview of some of the most important outcomes achieved in the framework of this project. The project provided metrological support to achieve the maximum synergistic advantages in the integration of radiotherapy oncology with different hyperthermia techniques, based on high intensity Therapeutic Ultrasound, Electromagnetic Radiation and the use of magnetic nanoparticles excited by AC magnetic fields.
• QUIERO Project “Quantitative MR-based imaging of physical biomarkers”.
  The session will provide an overview of some of the most important outcomes achieved in the framework of this project that investigated the use of two quantitative magnetic resonance imaging techniques (namely, Electric Properties Tomography and Magnetic Resonance Fingerprinting) for diagnostic purposes. Precisely, the presentations will show the performance of the adopted algorithms and will discuss the suitability of the biomarkers provided by them to classify different conditions of the imaged biological tissues.
• iMet-MRI Project “Improved Metrology for Quantitative MRI”.
  The session will focus on work under development within the Digital Tools and Data Work Package of the project, covering the modelling approaches developed to synthesise quantitative magnetic resonance imaging (qMRI) signals and images, as well as the data analysis quantification tools to extract quantitative measurands from qMRI images. Overall the project aims to develop test objects, traceable methods, analysis tools, and best practice guidance for various qMRI techniques and demonstrate them in an international multi-site trial.
• ViDiT Project “Trustworthy virtual experiments and digital twins”.
  The session will focus on the main scientific topics that will be addressed by this project, recently funded within the EPM Digital Transformation call. The use of virtual experiments and digital twins in metrological applications requires uncertainty evaluation methods, as well as reliable validation procedures, to make them fit, e.g., as substitutes or extensions to certified measurement devices. The project research aims to develop these methods and procedures to ensure the reliability and trustworthiness of virtual experiments and digital twins in metrology. To facilitate the uptake of the developed methods by industrial stakeholders, the transferability of the developed methods will be demonstrated for a large variety of industrial case studies covering several metrological applications.

• Applications of Machine Learning Methods for Solving Inverse Problems.
  Inverse problems deal with the estimation of causal factors from observed quantities. Because of recent advancements, machine learning methods are becoming increasingly popular for solving inverse problems. The session will present several such applications and discuss the benefits over traditional methods.

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