Publications related to Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Shapes from Pixels

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

A posteriori error estimations for elliptic partial differential equations with small uncertainties

Fabio Nobile, Marco Picasso, Diane Sylvie Guignard

In this article, a finite element error analysis is performed on a class of linear and nonlinear elliptic problems with small uncertain input. Using a perturbation approach, the exact (random) solution is expanded up to a certain order with respect to a pa ...

2016

Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output

Andreas Pautz, Damar Canggih Wicaksono, Omar Zerkak

The currently adopted practice for uncertainty quantification of thermal-hydraulics code predictions is done through statistical sampling where the code is evaluated multiple times using different values of input parameters that are randomly generated acco ...

2016

Influence of loading history on mechanical properties of biomaterials

Tom Charles Maurice Walet

It is known that the mechanical response to load or displacement of a biological tissue can highly vary from one cycle to the next. Therefore, in order to establish a reference state for repeating data measurements, tissues are subjected to a preliminary s ...

2016

MATHICSE Technical Report : A continuation multi level Monte Carlo (C-MLMC) method for uncertainty quantification in compressible aerodynamics

Fabio Nobile, Pénélope Leyland, Michele Pisaroni

In this work we apply the Continuation Multi-Level Monte Carlo (C-MLMC) algorithm proposed by [Collier et al, BIT 2014] to efficiently propagate operational and geometrical uncertainties in compressible aerodynamics numerical simulations. The key idea of M ...

MATHICSE2016

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

Ali H. Sayed

Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the t ...

IEEE2016

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

Agnieszka Katarzyna Miedlar

The first algorithms for computing the minimal Geršgorn set were developed by Varga et all. in [17] for the use on small and medium size (dense) matrices. Here, we first discuss the existing methods and present a new approach based on the modified Newton’s ...

MATHICSE2015

Application of the PSI-NUSS Tool for the Estimation of Nuclear Data Related k(eff) Uncertainties for the OECD/NEA WPNCS UACSA Phase I Benchmark

Andreas Pautz, Hakim Ferroukhi, Tiankai Zhu, Alexander Vasiliev

At the Paul Scherrer Institute (PSI), a methodology titled PSI-NUSS is under development for the propagation of nuclear data uncertainties into Criticality Safety Evaluation (CSE) with the Monte Carlo code MCNPX. The primary purpose is to provide a complem ...

Academic Press Inc Elsevier Science2014

Study of Nuclear Decay Data Contribution to Uncertainties in Heat Load Estimations for Spent Fuel Pools

Andreas Pautz, Mathieu Hursin, Hakim Ferroukhi, Alexander Vasiliev

At the Paul Scherrer Institut (PSI), a methodology for nuclear data uncertainty propagation in CASMO-5M (C5M) assembly calculations is under development. This paper presents a preliminary application of this methodology to C5M decay heat calculations. Appl ...

Academic Press Inc Elsevier Science2014

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

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Shapes from Pixels

A posteriori error estimations for elliptic partial differential equations with small uncertainties

Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output

Influence of loading history on mechanical properties of biomaterials

MATHICSE Technical Report : A continuation multi level Monte Carlo (C-MLMC) method for uncertainty quantification in compressible aerodynamics

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

Random sampling of bandlimited signals on graphs

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

Application of the PSI-NUSS Tool for the Estimation of Nuclear Data Related k(eff) Uncertainties for the OECD/NEA WPNCS UACSA Phase I Benchmark

Study of Nuclear Decay Data Contribution to Uncertainties in Heat Load Estimations for Spent Fuel Pools

Bayesian Calibration of Thermal-Hydraulics Model with Time-Dependent Output

A posteriori error estimations for elliptic partial differential equations with small uncertainties

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

Study of Nuclear Decay Data Contribution to Uncertainties in Heat Load Estimations for Spent Fuel Pools

Shapes from Pixels

Influence of loading history on mechanical properties of biomaterials

MATHICSE Technical Report : A continuation multi level Monte Carlo (C-MLMC) method for uncertainty quantification in compressible aerodynamics

Random sampling of bandlimited signals on graphs

MATHICSE Technical Report : An algorithm for computing minimal Geršgorin sets

Application of the PSI-NUSS Tool for the Estimation of Nuclear Data Related k(eff) Uncertainties for the OECD/NEA WPNCS UACSA Phase I Benchmark