The search session has expired. Please query the service again.
We present a domain decomposition theory on an interface problem for the linear transport equation between a diffusive and a non-diffusive region. To leading order, i.e. up to an error of the order of the mean free path in the diffusive region, the solution in the non-diffusive region is independent of the density in the diffusive region. However, the diffusive and the non-diffusive regions are coupled at the interface at the next order of approximation. In particular, our algorithm avoids iterating...
We present a domain decomposition theory on an interface problem
for the linear transport equation between a diffusive and a non-diffusive region.
To leading order, i.e. up to an error of the order of the mean free path in the
diffusive region, the solution in the non-diffusive region is independent of the
density in the diffusive region. However, the diffusive and the non-diffusive regions
are coupled at the interface at the next order of approximation. In particular, our
algorithm avoids iterating...
We present a Monte Carlo technique for sampling from the
canonical distribution in molecular dynamics. The method is built upon
the Nosé-Hoover constant temperature formulation and the generalized
hybrid Monte Carlo method. In contrast to standard hybrid Monte Carlo methods
only the thermostat degree of freedom is stochastically resampled
during a Monte Carlo step.
In this paper we present a novel exponentially fitted finite element
method with triangular elements for the decoupled continuity equations
in the drift-diffusion model of semiconductor devices.
The continuous problem is first formulated as a variational problem
using a weighted inner product. A Bubnov-Galerkin
finite element method with a set of piecewise exponential basis functions
is then proposed. The method is shown to be stable and can be regarded as
an extension to two dimensions of the...
The primary objective of this work is to develop coarse-graining
schemes for stochastic many-body microscopic models and quantify their
effectiveness in terms of a priori and a posteriori error analysis. In
this paper we focus on stochastic lattice systems of
interacting particles at equilibrium.
The proposed algorithms are derived from an initial coarse-grained
approximation that is directly computable by Monte Carlo simulations,
and the corresponding numerical error is calculated using the...
Generalized Shadow Hybrid Monte Carlo (GSHMC) is a method for molecular simulations that rigorously alternates Monte Carlo sampling from a canonical ensemble with integration of trajectories using Molecular Dynamics (MD). While conventional hybrid Monte Carlo methods completely re-sample particle’s velocities between MD trajectories, our method suggests a partial velocity update procedure which keeps a part of the dynamic information throughout the simulation. We use shadow (modified) Hamiltonians,...
We present an efficient approach for reducing the statistical uncertainty
associated with direct Monte Carlo simulations of the Boltzmann equation.
As with previous variance-reduction approaches, the resulting relative
statistical uncertainty in hydrodynamic quantities (statistical uncertainty normalized by the
characteristic value of quantity of interest) is small
and independent of the magnitude of the deviation from equilibrium,
making the simulation of arbitrarily small deviations from equilibrium
possible....
The paper studies the convergence behavior of
Monte Carlo schemes for semiconductors.
A detailed analysis of the systematic error
with respect to numerical parameters is performed.
Different sources of systematic error are pointed out and
illustrated in a spatially one-dimensional test case.
The error with respect to the number of simulation particles
occurs during the calculation of the internal electric field.
The time step error, which is related to the splitting of transport and
electric field...
Motivated by the development of efficient Monte Carlo methods
for PDE models in molecular dynamics,
we establish a new probabilistic interpretation of a family of divergence form
operators with discontinuous coefficients at the interface
of two open subsets of . This family of operators includes the case of the
linearized Poisson-Boltzmann equation used to
compute the electrostatic free energy of a molecule.
More precisely, we explicitly construct a Markov process whose
infinitesimal generator...
Through a Metropolis-like algorithm with single step computational cost of order one, we build a Markov chain that relaxes to the canonical Fermi statistics for k non-interacting particles among m energy levels. Uniformly over the temperature as well as the energy values and degeneracies of the energy levels we give an explicit upper bound with leading term km ln k for the mixing time of the dynamics. We obtain such construction and upper bound as a special case of a general result on (non-homogeneous)...
The purpose of the present article is to compare different phase-space
sampling methods,
such as purely stochastic methods (Rejection method, Metropolized
independence sampler, Importance Sampling),
stochastically perturbed Molecular Dynamics methods
(Hybrid Monte Carlo, Langevin Dynamics, Biased Random Walk), and purely
deterministic methods (Nosé-Hoover chains, Nosé-Poincaré and Recursive
Multiple Thermostats (RMT) methods). After recalling
some theoretical convergence properties for
the...
In this paper, we consider second order neutrons diffusion problem with
coefficients in L∞(Ω). Nodal method of the lowest
order is applied to approximate the problem's solution. The approximation
uses special basis functions [1] in which the coefficients
appear. The rate of convergence obtained is O(h2) in L2(Ω),
with a free rectangular triangulation.
Currently displaying 1 –
19 of
19