Results 21 to 30 of about 173,069 (310)
Modeling of lactose enzymatic hydrolysis using Monte Carlo method
Background: Mathematical modeling is useful in the analysis, prediction, and optimization of an enzymatic process. Unlike the conventional modeling methods, Monte Carlo method has special advantages in providing representations of the molecule’s spatial ...
Ling Gao +6 more
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The development of palm oil production is quite rapid in Indonesia. The method used to estimate inventory costs in this study is the Monte Carlo Simulation method. Monte Carlo simulation is used in structuring optimal raw material policies.
Cindy Artika +2 more
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Examining the Value of Monte Carlo Simulation for Project Time Management
Research Question: This paper investigates whether the Monte Carlo simulation can be widely used as a practicable method for the analysis of the risks that impact project duration.
Goran Avlijas
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Multilevel Monte Carlo methods [PDF]
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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This paper focuses on reducing the computational cost of the Monte Carlo method for uncertainty propagation. Recently, Multi-Fidelity Monte Carlo (MFMC) method (Ng, 2013; Peherstorfer et al., 2016) and Multi-Level Monte Carlo (MLMC) method (Müller et al.,
Nagoor Kani Jabarullah Khan +1 more
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Monte Carlo and Quasi Monte Carlo Approach to Ulam's Method for Position Dependent Random Maps
We consider position random maps $T=\{\tau_1(x),\tau_2(x),\ldots, \tau_K(x); p_1(x),p_2(x),\ldots,p_K(x)\}$ on $I=[0, 1],$ where $\tau_k, k=1, 2, \dots, K$ is non-singular map on $[0,1]$ into $[0, 1]$ and $\{p_1(x),p_2(x),\ldots,p_K(x)\}$ is a set of
Md Shafiqul Islam
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Semistochastic Projector Monte Carlo Method
We introduce a semistochastic implementation of the power method to compute, for very large matrices, the dominant eigenvalue and expectation values involving the corresponding eigenvector. The method is semistochastic in that the matrix multiplication is partially implemented numerically exactly and partially with respect to expectation values only ...
Petruzielo, F. R. +4 more
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The paper presents an introductory and general discussion on the quantum Monte Carlo methods, some fundamental algorithms, concepts and applicability. In order to introduce the quantum Monte Carlo method, preliminary concepts associated with Monte Carlo ...
Wagner Fernando Delfino Angelotti +3 more
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Comparation of nonuniform and uniform Monte - Carlo Searching
Nonuniform Monte-Carlo method is often used for optimization and solution of function mapping. This method has some disadvantages. New genetic algorithm, based on uniform Monte-Carlo is proposed by authors reduce disadvantage of nonuniform Monte- Carlo ...
Handrik Marián +3 more
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Optimized monte carlo methods [PDF]
I discuss optimized data analysis and Monte Carlo methods. Reweighting methods are discussed through examples, like Lee-Yang zeroes in the Ising model and the absence of deconfinement in QCD. I discuss reweighted data analysis and multi-hystogramming. I introduce Simulated Tempering, and as an example its application to the Random Field Ising Model.
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