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Multilevel Monte Carlo methods [PDF]
Acta Numerica, 2013 Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs.
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A comparative study between Classical Numerical Methods and Monte Carlo Methods [PDF]
المجلة العراقية للعلوم الاحصائية, 2008 Recently, there is a great interest in the methods of Monte Carlo used for the treatment of different technical and scientific issues. This research deals with using the Monte Carlo methods in numerical integration by making a general comparison between
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Metropolis Methods for Quantum Monte Carlo Simulations [PDF]
, 2003 Since its first description fifty years ago, the Metropolis Monte Carlo method has been used in a variety of different ways for the simulation of continuum quantum many-body systems.
Ceperley, D. M.
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Quantum Monte Carlo Methods in Statistical Mechanics [PDF]
, 1999 This paper deals with the optimization of trial states for the computation of dominant eigenvalues of operators and very large matrices. In addition to preliminary results for the energy spectrum of van der Waals clusters, we review results of the ...
Melik-Alaverdian, Vilen +1 more
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Convergence of Monte Carlo methods for neutron noise [PDF]
EPJ Web of ConferencesThe neutron noise δφ describes the small variations of the neutron flux around the stationary state φ0, and is typically due to vibrations or oscillations of the core components, induced by fluid-structure interactions and other generally unwanted ...
Fauvel Axel +3 more
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An LPC pole processing method for enhancing the identification of dominant spectral features
Electronics Letters, 2021 This paper proposes a new time‐resolved spectral analysis method based on a modification to the linear predictive coding (LPC) method for enhancing the identification of the dominant frequencies of a signal.
Jin Xu, Mark Davis, Ruairí de Fréin
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Automatic differentiable Monte Carlo: Theory and application
Physical Review Research, 2023 Differentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo remain largely unexplored.
Shi-Xin Zhang, Zhou-Quan Wan, Hong Yao
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Monte Carlo Methods for the Shapley–Shubik Power Index
Games, 2022 This paper deals with the problem of calculating the Shapley–Shubik power index in weighted majority games. We propose an efficient Monte Carlo algorithm based on an implicit hierarchical structure of permutations of players.
Yuto Ushioda +2 more
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GENERALIZED SENSITIVITY ANALYSIS CAPABILITY WITH THE DIFFERENTIAL OPERATOR METHOD IN RMC CODE [PDF]
EPJ Web of Conferences, 2021 Sensitivity analysis is an important way for us to know how the input parameters will affect the output of a system. Therefore, recently, there is an increased interest in developing sensitivity analysis methods in continuous-energy Monte Carlo Code due ...
Shi Guanlin +3 more
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Sequential Monte Carlo Methods for System Identification [PDF]
, 2015 One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state.
Dahlin, Johan +6 more
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