Results 21 to 30 of about 2,973,401 (293)

Uncertainty Analysis of Neutron Diffusion Eigenvalue Problem Based on Reduced-order Model

Yuanzineng kexue jishu, 2023
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model ...
In order to improve the efficiency of core physical uncertainty analysis based on sampling statistics, the proper orthogonal decomposition (POD) and Galerkin projection method were combined to study the application feasibility of reduced-order model based on POD-Galerkin method in core physical uncertainty analysis. The two-dimensional two group TWIGL benchmark question was taken as the research object, the key variation characteristics of the core flux distribution were extracted under the finite perturbation of the group constants of each material region, and the full-order neutron diffusion problem was projected on the variation characteristics to establish a reduced-order neutron diffusion model. The reduced-order model was used to replace the full-order model to carry out the uncertainty analysis of the group constants of the material region. The results show that the bias of the mathematical expectation of keff calculated by reduced-order and full-order models is close to 1 pcm. In addition, compared with the calculation time required for uncertainty analysis of full-order model, the analysis time of reduced-order model (including the calculation time of the full-order model required for the construction of reduced-order model) is only 11.48%, which greatly improves the efficiency of uncertainty analysis. The biases of mathematical expectation of keff calculated by reduced-order and full-order models based on Latin hypercube sampling and simple random sampling are less than 8 pcm, and under the same sample size, the bias from the Latin hypercube sampling result is smaller. From the TWIGL benchmark test results, under the same sample size, Latin hypercube sampling method is more recommended for POD-Galerkin reduced-order model.
doaj  

Superreplication under Model Uncertainty in Discrete Time [PDF]

, 2014
We study the superreplication of contingent claims under model uncertainty in discrete time. We show that optimal superreplicating strategies exist in a general measure-theoretic setting; moreover, we characterize the minimal superreplication price as ...
Nutz, Marcel
core   +2 more sources

Consistent Price Systems under Model Uncertainty [PDF]

, 2014
We develop a version of the fundamental theorem of asset pricing for discrete-time markets with proportional transaction costs and model uncertainty.
Bouchard, Bruno, Nutz, Marcel
core   +3 more sources

Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings [PDF]

, 2011
Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty.
A Quinn, A Wagner, AC Courville, AJ Yu, AN Hampton, BA Strange, CD Fiorillo, D Draper, D Ellsberg, E Payzan-LeNestour, Elise Payzan-LeNestour, FH Knight, G Aston-Jones, G Vanni-Mercier, GI Christopoulos, J Dow, JD Cohen, JM Keynes, JM Pearce, JO Berger, K Craik, K Doya, K Preuschoff, K Preuschoff, K Sangjoon, LP Hansen, M Allais, M Basili, M d'Acremont, M Hsu, MFS Rushworth, MP Paulus, ND Daw, ND Daw, P Bossaerts, P Dayan, P Diaconis, Peter Bossaerts, PN Tobler, RE Kass, RH Thaler, S Huettel, S Ishii, S Kakade, SA Huettel, TEJ Behrens, Tim Behrens, U Rutishauser, W Epstein, W Yoshida   +49 more
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Adaptive Neural Network Control for Exoskeleton Motion Rehabilitation Robot With Disturbances and Uncertain Parameters

IEEE Access, 2023
This paper investigates the adaptive control problem for a class of Euler-Lagrangian (EL) systems with uncertain parameters and external disturbances.
Bowen Zhang, Tong Wu, Tianqi Wang
doaj   +1 more source

Embedded model control, performance limits: A case study

Dyna, 2017
This paper presents the analysis and implementation of two control laws applied on a case study. The first one and main focus of this work is the Embedded Model Control whose main characteristics are the active disturbance rejection and uncertainties ...
Wilber Acuña-Bravo, Andrés Guillermo Molano-Jiménez, Enrico Canuto   +2 more
doaj   +1 more source

Model Uncertainty in the Projected Indian Summer Monsoon Precipitation Change under Low-Emission Scenarios

Atmosphere, 2021
The projected ISM precipitation changes under low-emission scenarios, Representative Concentration Pathway 2.6 (RCP2.6) and Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6), are investigated by outputs from models participating in phases 5 and 6 of the ...
Shang-Min Long, Gen Li
doaj   +1 more source

Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

Energies, 2022
In this study, the crude oil spot price is forecast using Bayesian symbolic regression (BSR). In particular, the initial parameters specification of BSR is analysed.
Krzysztof Drachal
doaj   +1 more source

To the brave scientists: Aren't we strong enough to stand (and profit from) uncertainty in Earth system measurement and modelling?

Geoscience Data Journal, 2022
The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty.
Hendrik Paasche, Matthias Gross, Jakob Lüttgau, David S. Greenberg, Tobias Weigel   +4 more
doaj   +1 more source

Uncertainty Quantification of Future Design Rainfall Depths in Korea [PDF]

, 2019
One of the most common ways to investigate changes in future rainfall extremes is to use future rainfall data simulated by climate models with climate change scenarios.
Handmer, Kim, Lee, McCuen, Prodanovic, Skamarock, Solaiman   +6 more
core   +1 more source