990 resultados para MONTE CARLOS METHOD
Resumo:
Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks arising from multimodal sampling, neural network training, and the traveling salesman problem. Numerical tests on these problems confirm the effectiveness of the method.
Resumo:
"July 1979."
Resumo:
Thesis--University of Illinois at Urbana-Champaign.
Resumo:
"Task No. 70717"
Resumo:
"UNC-5014 (Volume A) Final Report covering the period 20 March 1961 - 31 May 1962."
Resumo:
"Performing organization: Oklahoma State University, College of Business Administration , Stillwater."
Resumo:
∗This research, which was funded by a grant from the Natural Sciences and Engineering Research Council of Canada, formed part of G.A.’s Ph.D. thesis [1].
Resumo:
Добри Данков, Владимир Русинов, Мария Велинова, Жасмина Петрова - Изследвана е химическа реакция чрез два начина за моделиране на вероятността за химическа реакция използвайки Direct Simulation Monte Carlo метод. Изследван е порядъка на разликите при температурите и концентрациите чрез тези начини. Когато активността на химическата реакция намалява, намаляват и разликите между концентрациите и температурите получени по двата начина. Ключови думи: Механика на флуидите, Кинетична теория, Разреден газ, DSMC
Resumo:
MSC Subject Classification: 65C05, 65U05.
Resumo:
The Monte Carlo method is accurate and is relatively simple to implement for the solution of problems involving complex geometries and anisotropic scattering of radiation as compared with other numerical techniques. In addition, differently of what happens for most of numerical techniques, for which the associated simulations computational time tends to increase exponentially with the complexity of the problems, in the Monte Carlo the increase of the computational time tends to be linear. Nevertheless, the Monte Carlo solution is highly computer time consuming for most of the interest problems. The Multispectral Energy Bundle model allows the reduction of the computational time associated to the Monte Carlo solution. The referred model is here analyzed for applications in media constituted for nonparticipating species and water vapor, which is an important emitting species formed during the combustion of hydrocarbon fuels. Aspects related to computer time optimization are investigated the model solutions are compared with benchmark line-by-line solutions
Resumo:
Tese (Doutoramento)