Uncertainty contribution estimation for Monte Carlo uncertainty quantification via subspace analysis: neutronics case study

2021 ◽  
Author(s):  
Bassam Khuwaileh ◽  
Mohammad Al-Shabi ◽  
Mamdouh El Haj Assad
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Subspace Analysis ◽  
Uncertainty Contribution

scholarly journals A combined Monte Carlo/ DFT approach to simulate UV ‐vis spectra of molecules and aggregates: Merocyanine dyes as a case study

2021 ◽  
Author(s):  
Bernardino Tirri ◽  
Gloria Mazzone ◽  
Alistar Ottochian ◽  
Jerôme Gomar ◽  
Umberto Raucci ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Merocyanine Dyes

scholarly journals Toward a systematic improvement of the fixed-node approximation in diffusion Monte Carlo for solids—A case study in diamond

2020 ◽  
Vol 153 (18) ◽  
pp. 184111
Author(s):  
Anouar Benali ◽  
Kevin Gasperich ◽  
Kenneth D. Jordan ◽  
Thomas Applencourt ◽  
Ye Luo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Diffusion Monte Carlo ◽  
Systematic Improvement ◽  
Fixed Node

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

2013 ◽  
Vol 50 ◽  
pp. 4-15 ◽  
Author(s):  
D. Arnold ◽  
V. Demyanov ◽  
D. Tatum ◽  
M. Christie ◽  
T. Rojas ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
History Matching

scholarly journals A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saba Moeinizade ◽  
Ye Han ◽  
Hieu Pham ◽  
Guiping Hu ◽  
Lizhi Wang
Keyword(s):  
Monte Carlo ◽  
State Of The Art ◽  
Simulation Method ◽  
Multiple Trait ◽  
Look Ahead ◽  
Probability Of Success ◽  
Genetic Distribution ◽  
Selection Of ◽  
Trait Introgression

AbstractMultiple trait introgression is the process by which multiple desirable traits are converted from a donor to a recipient cultivar through backcrossing and selfing. The goal of this procedure is to recover all the attributes of the recipient cultivar, with the addition of the specified desirable traits. A crucial step in this process is the selection of parents to form new crosses. In this study, we propose a new selection approach that estimates the genetic distribution of the progeny of backcrosses after multiple generations using information of recombination events. Our objective is to select the most promising individuals for further backcrossing or selfing. To demonstrate the effectiveness of the proposed method, a case study has been conducted using maize data where our method is compared with state-of-the-art approaches. Simulation results suggest that the proposed method, look-ahead Monte Carlo, achieves higher probability of success than existing approaches. Our proposed selection method can assist breeders to efficiently design trait introgression projects.

Jean-Christophe Maillot

2021 ◽  
pp. 213-228
Author(s):  
Laura Cappelle
Keyword(s):  
Monte Carlo ◽  
Early Development ◽  
Contemporary Dance ◽  
Hybrid Nature ◽  
International Recognition ◽  
Practical Support ◽  
Bolshoi Ballet ◽  
Large Repertoire

Jean-Christophe Maillot is one of the few French choreographers to have achieved international recognition in the field of contemporary ballet in recent years. This chapter explores his fraught early development as a classically trained artist in the midst of a contemporary dance boom in France and his subsequent career at the helm of Les Ballets de Monte-Carlo. There, he found the practical support and creative freedom to build a large repertoire, both narrative and abstract, from 1993 onward. Finally, Maillot’s process and style are explored through a case study: The Taming of the Shrew, a ballet he created for Moscow’s Bolshoi Ballet in 2014. On the basis of studio-based sociological observations and interviews conducted during the rehearsals, this creation is envisioned as an example of the hybrid nature of new works in ballet today and the influence of the environment in which they are made.

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