Extrapolation of the time-step bias in diffusion quantum Monte Carlo by a differential sampling technique

1989 ◽  
Vol 10 (2) ◽  
pp. 176-185 ◽  
Author(s):  
David R. Garmer
Keyword(s):  
Monte Carlo ◽  
Quantum Monte Carlo ◽  
Sampling Technique ◽  
Time Step

scholarly journals Boosting the accuracy and speed of quantum Monte Carlo: Size consistency and time step

2016 ◽  
Vol 93 (24) ◽  
Author(s):  
Andrea Zen ◽  
Sandro Sorella ◽  
Michael J. Gillan ◽  
Angelos Michaelides ◽  
Dario Alfè
Keyword(s):  
Monte Carlo ◽  
Quantum Monte Carlo ◽  
Time Step ◽  
Size Consistency

scholarly journals Is there a zeroth order time‐step error in diffusion quantum Monte Carlo?

1987 ◽  
Vol 87 (3) ◽  
pp. 1905-1906 ◽  
Author(s):  
P. J. Reynolds ◽  
R. K. Owen ◽  
W. A. Lester
Keyword(s):  
Monte Carlo ◽  
Quantum Monte Carlo ◽  
Zeroth Order ◽  
Time Step

scholarly journals Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2328
Author(s):  
Mohammed Alzubaidi ◽  
Kazi N. Hasan ◽  
Lasantha Meegahapola ◽  
Mir Toufikur Rahman
Keyword(s):  
Monte Carlo ◽  
Stability Analysis ◽  
Power Systems ◽  
Large Scale ◽  
Voltage Stability ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Sampling Techniques ◽  
Quasi Monte Carlo ◽  
Efficient Sampling

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

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