scholarly journals An exact framework for uncertainty quantification in Monte Carlo simulation

2014 ◽  
Vol 513 (2) ◽  
pp. 022033 ◽  
Author(s):  
P Saracco ◽  
M G Pia
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Uncertainty Quantification

scholarly journals Commuting-Adjusted Short-Term Health Impact Assessment of Airborne Fine Particles with Uncertainty Quantification via Monte Carlo Simulation

2015 ◽  
Vol 123 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Michela Baccini ◽  
Laura Grisotto ◽  
Dolores Catelan ◽  
Dario Consonni ◽  
Pier Alberto Bertazzi ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Impact Assessment ◽  
Uncertainty Quantification ◽  
Health Impact Assessment ◽  
Fine Particles ◽  
Health Impact ◽  
Short Term

scholarly journals Uncertainty Quantification in Natural Frequency of Composite Plates - An Artificial Neural Network Based Approach

2016 ◽  
Vol 25 (2) ◽  
pp. 096369351602500 ◽  
Author(s):  
Sudip Dey ◽  
Tanmoy Mukhopadhyay ◽  
Axel Spickenheuer ◽  
Uwe Gohs ◽  
S. Adhikari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Natural Frequency ◽  
Composite Plates ◽  
Ann Model ◽  
Artificial Neural ◽  
Natural Frequency Analysis

This paper presents the stochastic natural frequency for laminated composite plates by using artificial neural network (ANN) model. The ANN model is employed as a surrogate and is trained by using Latin hypercube sampling. Subsequently the stochastic first two natural frequencies are quantified with ANN based uncertainty quantification algorithm. The convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM) in conjunction with Monte Carlo simulation. Both individual and combined variation of stochastic input parameters are considered to address the influence on the output of interest. The sample size and computational cost are reduced by employing the present approach compared to traditional Monte Carlo simulation.

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