# Séverine Demeyer's research while affiliated with Laboratoire National de Métrologie et d'Essais and other places

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## Publications (26)

Thermal management is a key issue for the downsizing of electronic components in order to optimise their performance. These devices incorporate more and more nanostructured materials, such as thin films or nanowires, requiring measurement techniques suitable to characterise thermal properties at the nanoscale, such as Scanning Thermal Microscopy (S...

INSIDER (Improved Nuclear SIte characterization for waste minimization in D&D operations under constrained EnviRonment) was a European project funded under the H2020-EURATOM programme and launched in June 2017. The project was coordinated by the French Commissariat à l’énergie atomique et aux énergies alternatives (CEA), it had a total duration of...

In order to further improve the management of contaminated materials in nuclear facilities subject to a decommissioning programme, as well as during post-accidental site remediation and clearance, the definition and selection of the most appropriate intervention scenarios producing well-characterized radioactive waste for which storage and disposal...

In this document, the examples illustrate various aspects of uncertainty evaluation and the use of uncertainty statements in conformity assessment. These aspects include, but are not limited to
– choice of the mechanism for propagating measurement uncertainty,
– reporting measurement results and measurement uncertainty,
– conformity assessment, and...

This set of examples addresses measurement in healthcare in the following topic areas. The examples show improved and alternative treatments of the evaluation of measurement uncertainty, building on current practice in these areas. A diversity of topics is addressed, such as uncertainty arising in image reconstruction, determination of nanoparticle...

In this work, we propose a fully Bayesian uncertainty analysis of the indirect measurement of thermal properties of walls from in-situ temperature and flux measurements, obtained with an active method, using a one dimensional transient thermal model. We show that this approach is able to take into account the uncertainty of the inputs of the therma...

This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obt...

The importance of measurement quality cannot be over emphasized in medical applications, as one is dealing with life issues and the wellbeing of society, from oncology to new-borns, and more recently to patients of the COVID-19 pandemic. In all these dire situations, the accuracy of fluid delivered according to a prescribed dose can be critical.
Mi...

In this paper we provide guidance on a Bayesian uncertainty evaluation for a large class of GUM measurement models covering linear and non-linear models. Bayesian analysis takes advantage of useful prior knowledge on the measurand, which is often available from a metrologist’s genuine expertise and opinion, or from previous experiments and which is...

This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Very often, accurate simulations correspond to high computational efforts whereas coarse simulati...

A multi-fidelity simulator is a numerical model, in which one of the inputs controls a trade-off between the realism and the computational cost of the simulation. Our goal is to estimate the probability of exceeding a given threshold on a multi-fidelity stochastic simulator. We propose a fully Bayesian approach based on Gaussian processes to comput...

In this article, we consider a stochastic numerical simulator to assess the impact of some factors on a phenomenon. The simulator is seen as a black box with inputs and outputs. The quality of a simulation, hereafter referred to as fidelity, is assumed to be tunable by means of an additional input of the simulator (e.g., a mesh size parameter): hig...

Nowadays, fire safety engineers are increasingly relying on sophisticated numerical simulators, typically based on Computational Fluid Dynamics (CFD) solvers, to conduct their analyses. However, the complexity of these numerical models often limits drastically the number of simulations that can be afforded, making traditional methods of safety anal...

In this paper, we propose a Bayesian framework to analyse proficiency tests results that allows to combine prior information on laboratories and prior knowledge on the consensus value when no measurement uncertainties nor replicates are reported. For these proficiency tests, where the reported data is reduced to its minimum, we advocate that each p...

To assess the possibility of evacuating a building in case of a fire, a standard method consists in simulating the propagation of fire, using finite difference methods and takes into account the random behavior of the fire, so that the result of a simulation is non-deterministic. The mesh fineness tunes the quality of the numerical model, and its c...

Decisions based on measurement, such as tests of the significance of differences and of conformity, are currently made in many important application areas without a clear and harmonised basis for assessing the impact and sharing the risks that arise from measurement uncertainty. The need for improved insight into setting a ‘fit-for-purpose’ level o...

Evaluation des incertitudes associéesassociéesà la mesure granulométrique d'un aérosol par technique SMPS. JdS Résumé. La détermination de la granulométrie en nombre d'un aérosol (concentration en nombre de particules en fonction du diamètre) à partir de mesures effectuées par un SMPS (Scanning Mobility Particle Sizer) est un problème mathématiquem...

In this paper auxiliary information on laboratories is combined with proficiency testing (PT) data to compute more reliable consensus values and associated uncertainties. This new methodology extensively relies on expert knowledge to assume measurement bias, to investigate the sources of measurement bias, to model relationships between sources of b...

Structural Equation Models with latent variables (SEM) are hypothetical constructs
used to represent causality relationships in data, where the observed
correlation structure is transferred into the correlation structure of latent variables.
In this paper a Bayesian analysis of SEM is proposed using parameter
expansion to overcome identi fiability...

Structural equation modelling is a widespread approach in a variety of domains and is first applied here to interlaboratory comparisons in metrology. Structural Equation Models with latent variables (SEM) are multivariate models used to model causality relationships in observed variables (the data). It is assumed that data can be grouped into separ...

Les modèles à équations structurelles (SEMs) sont des modèles multivariés à variables latentes utilisés pour modéliser les structures de causalité dans les données. Une approche bayésienne d'estimation et de validation des modèles SEMs est proposée et l'identifiabilité des paramètres est étudiée. Cette étude montre qu'une étape de réduction des var...

Structural equation models (SEMs) are multivariate latent variable models used to model causality structures in data. A Bayesian estimation and validation of SEMs is proposed and identifiability of parameters is studied. The latter study shows that latent variables should be standardized in the analysis to ensure identifiability. This heuristics is...

On propose une nouvelle approche dans l'estimation des biais, c'est à dire des écarts entre résultats de laboratoires et la valeur vraie inconnue d'une grandeur, lors de comparaisons interlaboratoires. Pour cela on combine un modèle de régression hiérarchique des biais et un modèle à équations structurelles pour les caractéristiques des laboratoire...

## Citations

... As reported by van der Heen and Cox [15] sometimes national measurement laboratories may omit or ignore correlations because it is either too difficult to compute or for other reasons and this omission can lead to poor measurement decisions or logical absurdities when determining measurement results and equivalences such as in key comparison reference values (KCRVs) which are used to produce national laboratory standards. ...

... To avoid the ill-posed nature of the problem, different strategies can be considered. One can used a maximum of a priori information like the wall thickness, sensitivity analysis methods to limit the model updating to the most significant model parameters [38][39][40][41] and regularization techniques such as Tikhonov regularization [36,[42][43][44] or Bayesian framework [7,27,36,45,46]. According to the numerical study in [36], only the temperature on the internal surface of the studied wall (noted T SI ) is considered in the inversion process. ...

... Kuya et al. [2011] and Xiong et al. [2013] adapt this to perform a sequential design on the lowest level, and then take a subset from this for the design to be run at higher levels. Stroh et al. [2022] aims to select new points based on maximising the ratio between the expected reduction of uncertainty and the cost of running the computer code. Our criteria optimises for exploration and exploitation simultaneously, as well as offering a non-nested design approach. ...

... Measurement uncertainty studies on syringe pumps and insulin pumps have shown that evaporation is one of the biggest factors influencing measurement uncertainty [14,15]. The influence of evaporation increases at the lower flow rates. ...

... The application of the Bayesian paradigm to inverse problems has the advantage that probability statements about the quantity of interest can be made conditional on the observations. Moreover, it allows prior knowledge to be employed [12,13,14]. This prior knowledge, in terms of a probability distribution, reflects one's state of knowledge regarding the involved quantities and provides a natural stabilisation of the inverse problem, addressing classical ill-posedness [15]. ...

... For the numerical experiments of this article (Sections 4.3 and 4.4) we will take a simpler route, assuming that the variance λ depends only on the fidelity level δ-which is approximately true in the two examples we shall consider. In this setting, as long as the number of fidelity levels of interest is not too large, the value of the variance at these levels can be simply estimated jointly with the other hyper-parameters of the model; a general-purpose log-normal prior for the vector of variances is proposed by Stroh et al. (2016Stroh et al. ( , 2017b. ...

... The exact definition of the value of information depends on one's goals. For example, one could be interested in optimizing an objective [40,31,17,30,39,16,44,4,28,36,42,34,10], learning an accurate representation of the physical response [41,33,59,5,20,61] or estimating the probability of a rare event [45,46]. ...

... Recently, surrogate models based on multi-fidelity approaches have gained considerable interest in the literature [25][26][27], among others]. This approach allows to reduce the number of high-fidelity simulations by the addition of a second model, faster and less accurate to the inputoutput database. ...

... CSEs formulated with qualitative explanatory variables can also be performed, provided Rasch transformation is done [22]. Earlier, we have tested how a CSE could be obtained with qualitative explanatory variables for a measure of patient experiences of participating in care and rehabilitation [23], which in part corresponds to the method presented by Adroher and Tennant [15]. ...

... To this day, several techniques allowing to control the discretization error on the probability of failure exist. First, mesh convergence analysis can be performed on the probability of failure Alvin (2000); Demeyer et al. (2017); Ghavidel et al. (2020). However, it can lead to a huge computational cost as the probability of failure is computed for several meshes. ...