Results 31 to 40 of about 1,455,452 (353)

Simulação do equilíbrio: o método de Monte Carlo Equilibrium simulation: Monte Carlo method

Química Nova, 2007
We make several simulations using the Monte Carlo method in order to obtain the chemical equilibrium for several first-order reactions and one second-order reaction. We study several direct, reverse and consecutive reactions.
Alejandro López-Castillo, José Cândido de Souza Filho   +1 more
doaj   +1 more source

ANALISIS PENGENDALIAN PERSEDIAAN BAHAN BAKU TANDAN BUAH SEGAR (TBS) DENGAN METODE SIMULASI MONTE CARLO

Jurnal Lebesgue, 2023
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, Sajaratud Dur, Ismail Husein   +2 more
doaj   +1 more source

Examining the Value of Monte Carlo Simulation for Project Time Management

Management, 2019
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
doaj   +1 more source

Benchmark of a new multi-ion-species collision operator for $\delta f$ Monte Carlo neoclassical simulation [PDF]

, 2020
A numerical method to implement a linearized Coulomb collision operator in the two-weight $\delta f$ Monte Carlo method for multi-ion-species neoclassical transport simulation is developed.
122862, 123636, 126346, 126422, 67562, 67563, 67564, 67588, Ishiguro, Seiji, KANNO, Ryutaro, MATSUOKA, Seikichi, Nakata, Motoki, NUNAMI, Masanori, Pianpanit, Theerasarn, SATAKE, Shinsuke, SUGAMA, Hideo   +15 more
core   +3 more sources

Numerical approximation of statistical solutions of scalar conservation laws

, 2014
We propose efficient numerical algorithms for approximating statistical solutions of scalar conservation laws. The proposed algorithms combine finite volume spatio-temporal approximations with Monte Carlo and multi-level Monte Carlo discretizations of ...
Barrett, Paul M., Butler, Richard J., Irmis, Randall B., Mundil, Roland, Scheyer, Torsten M., Sánchez-Villagra, Marcelo R.   +5 more
core   +2 more sources

A Machine Learning Based Hybrid Multi-Fidelity Multi-Level Monte Carlo Method for Uncertainty Quantification

Frontiers in Environmental Science, 2019
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, Ahmed H. Elsheikh   +1 more
doaj   +1 more source

The Coupled Electronic-Ionic Monte Carlo Simulation Method [PDF]

, 2002
Quantum Monte Carlo (QMC) methods such as Variational Monte Carlo, Diffusion Monte Carlo or Path Integral Monte Carlo are the most accurate and general methods for computing total electronic energies.
B. L. Hammond Jr., B. Militzer, B. Militzer, C. Lin, C. Pierleoni, D. Hohl, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. M. Ceperley, D. Marx, D. Saumon, D. Saumon, D. Saumon, I. F. Silvera, J. C. Grossman, J. Kohanoff, J. Kohanoff, M. C. Payne, M. Dewing, M. E. Tuckerman, M. Sprik, N. C. Holmes, N. Metropolis, P. Diep, P. Diep, P. J. Reynolds, R. Car, V. Natoli, V. Natoli, W. J. Nellis, W. M. C. Foulkes, Y. Kwon, Y. Kwon   +35 more
core   +2 more sources

Monte Carlo and Quasi Monte Carlo Approach to Ulam's Method for Position Dependent Random Maps

Communications in Advanced Mathematical Sciences, 2020
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
doaj   +1 more source

Antithetic Riemannian Manifold And Quantum-Inspired Hamiltonian Monte Carlo [PDF]

arXiv, 2021
Markov Chain Monte Carlo inference of target posterior distributions in machine learning is predominately conducted via Hamiltonian Monte Carlo and its variants. This is due to Hamiltonian Monte Carlo based samplers ability to suppress random-walk behaviour.
arxiv  

Data‐driven performance metrics for neural network learning

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +1 more source