Publication

Convergence Analysis and Criterion for Data Assimilation with Sensitivities from Monte Carlo Neutron Transport Codes

Publications associées (31)

Learning new physics from an imperfect machine

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties a ...

SPRINGER2022

A Whale's Tail - Finding the Right Whale in an Uncertain World

Devis Tuia

Explainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information about th ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

Yuqing Liu, Xianlin Wang

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (AT ...

2022

Radiation Hardness Study of Single-Photon Avalanche Diode for Space and High Energy Physics Applications

Edoardo Charbon, Francesco Gramuglia, Kazuhiro Morimoto, Ekin Kizilkan, Pouyan Keshavarzian, Ming-Lo Wu, Carlo Alberto Fenoglio, Emanuele Ripiccini

The radiation hardness of 180 nm complementary metal-oxide-semiconductor (CMOS) and 55 nm bipolar-CMOS-double-diffused MOS single-photon avalanche diodes (SPADs) is studied using 10 MeV and 100 MeV protons up to a displacement damage dose of 1 PeV/g. It is ...

MDPI2022

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmark ...

EPFL2022

Modelling the Intracellular Environment under Uncertainty, from Post-translational Modification to Metabolic Networks

Robin Alexander Denhardt-Eriksson

Modelling and analysis of biological systems is crucial in order to quantitatively explain and predict their behaviour. The importance of modelling in systems biology becomes even more evident when tackling complex, large systems. Approaches that rely on ...

EPFL2021

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

Lubna Dada

Bayesian state estimation in the form of Kalman smoothing was applied to differential mobility analyser train (DMA-train) measurements of aerosol size distribution dynamics. Four experiments were analysed in order to estimate the aerosol size distribution, ...

Copernicus Publications2021

Experimental characterisation of textile compaction response: A benchmark exercise

Véronique Michaud, Christophe Lopez, Baris Çaglar

This paper reports the results of an international benchmark exercise on the measurement of fibre bed compaction behaviour. The aim was to identify aspects of the test method critical to obtain reliable results and to arrive at a recommended test procedure ...

2021

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Giacomo Garegnani

In this thesis we explore uncertainty quantification of forward and inverse problems involving differential equations. Differential equations are widely employed for modeling natural and social phenomena, with applications in engineering, chemistry, meteor ...

EPFL2021

Identification of optimal measurement points for energy monitoring of industrial processes: The case of milk powder production

Tuong-Van Nguyen

Meaningful energy analysis of industrial processes requires the installation of energy monitoring systems. However, a lack of systematic methods for identifying the required measurement points, joint to scarce information on the related benefits, results i ...

ELSEVIER SCI LTD2021

Uncertainty analyses of spent nuclear fuel decay heat calculations using SCALE modules

Stefano Caruso, Andreas Pautz, Dimitri Rochman

Decay heat residuals of spent nuclear fuel (SNF), i.e., the differences between calculations and measurements, were obtained previously for various spent fuel assemblies (SFA) using the Polaris module of the SCALE code system. In this paper, we compare dec ...

2021