The Experts below are selected from a list of 87435 Experts worldwide ranked by ideXlab platform
S A Matveev - One of the best experts on this subject based on the ideXlab platform.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
N A Gentile - One of the best experts on this subject based on the ideXlab platform.
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implicit monte carlo diffusion an Acceleration Method for monte carlo time dependent radiative transfer simulations
Journal of Computational Physics, 2001Co-Authors: N A GentileAbstract:Abstract Implicit Monte Carlo (IMC) is often employed to numerically simulate radiative transfer. In problems with regions that are characterized by a small mean free path, IMC can take a prohibitive amount of time, because many particle steps must be simulated to advance the particle through the time step. Problems containing regions with a small mean free path can frequently be accurately simulated much more quickly by employing the diffusion equation as an approximation. However, the diffusion approximation is not accurate in regions of the problem where the mean free path is large. We present a Method for accelerating time-dependent Monte Carlo radiative transfer calculations by using a discretization of the diffusion equation to calculate probabilities that are used to advance particles in regions with small mean free paths. The Method is demonstrated on problems with one-and two-dimensional orthogonal grids. It results in decreases in run time of more than an order of magnitude on these problems, while producing answers with accuracy comparable to pure IMC simulations. We call the Method Implicit Monte Carlo Diffusion, which we abbreviate IMD.
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implicit monte carlo diffusion an Acceleration Method for monte carlo time dependent radiative transfer simulations
Nuclear Explosives Code Developers Conference Oakland CA (US) 10 23 2000--10 27 2000, 2000Co-Authors: N A GentileAbstract:We present a Method for accelerating time dependent Monte Carlo radiative transfer calculations by using a discretization of the diffusion equation to calculate probabilities that are used to advance particles in regions with small mean free path. The Method is demonstrated on problems with on 1 and 2 dimensional orthogonal grids. It results in decreases in run time of more than an order of magnitude on these problems, while producing answers with accuracy comparable to pure IMC simulations. We call the Method Implicit Monte Carlo Diffusion, which we abbreviate IMD.
Nikolai V Brilliantov - One of the best experts on this subject based on the ideXlab platform.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
E E Tyrtyshnikov - One of the best experts on this subject based on the ideXlab platform.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
A P Smirnov - One of the best experts on this subject based on the ideXlab platform.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
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anderson Acceleration Method of finding steady state particle size distribution for a wide class of aggregation fragmentation models
Computer Physics Communications, 2017Co-Authors: S A Matveev, Vladimir I Stadnichuk, E E Tyrtyshnikov, A P Smirnov, N V Ampilogova, Nikolai V BrilliantovAbstract:Abstract A fast numerical Method of finding steady-state distributions of particles sizes for a wide class of aggregation–fragmentation models, including the models with a source of monomers is developed. The Method is based on a fast evaluation scheme for large sets of non-linear Smoluchowski-type ODE with an application of the Anderson Acceleration Method of the fixed point iterations. In the numerical tests of the suggested approach we demonstrate that huge sets of non-linear ODE may be solved with high precision in terms of Euclidian norm of the residual in modest times with use of a regular desktop computer. We compare our numerical solutions with the known analytical results for the steady-state distributions as well as with the other fast numerical schemes and prove the high accuracy of the novel Method and its significant superiority with respect to the existing fast numerical Method of solution of the addressed problems.
