243 research outputs found
A micro/macro parallel-in-time (parareal) algorithm applied to a climate model with discontinuous non-monotone coefficients and oscillatory forcing
We present the application of a micro/macro parareal algorithm for a 1-D
energy balance climate model with discontinuous and non-monotone coefficients
and forcing terms. The micro/macro parareal method uses a coarse propagator,
based on a (macroscopic) 0-D approximation of the underlying (microscopic) 1-D
model. We compare the performance of the method using different versions of the
macro model, as well as different numerical schemes for the micro propagator,
namely an explicit Euler method with constant stepsize and an adaptive library
routine. We study convergence of the method and the theoretical gain in
computational time in a realization on parallel processors. We show that, in
this example and for all settings, the micro/macro parareal method converges in
fewer iterations than the number of used parareal subintervals, and that a
theoretical gain in performance of up to 10 is possible
Asymptotic-Preserving Monte Carlo methods for transport equations in the diffusive limit
We develop a new Monte Carlo method that solves hyperbolic transport
equations with stiff terms, characterized by a (small) scaling parameter. In
particular, we focus on systems which lead to a reduced problem of parabolic
type in the limit when the scaling parameter tends to zero. Classical Monte
Carlo methods suffer of severe time step limitations in these situations, due
to the fact that the characteristic speeds go to infinity in the diffusion
limit. This makes the problem a real challenge, since the scaling parameter may
differ by several orders of magnitude in the domain. To circumvent these time
step limitations, we construct a new, asymptotic-preserving Monte Carlo method
that is stable independently of the scaling parameter and degenerates to a
standard probabilistic approach for solving the limiting equation in the
diffusion limit. The method uses an implicit time discretization to formulate a
modified equation in which the characteristic speeds do not grow indefinitely
when the scaling factor tends to zero. The resulting modified equation can
readily be discretized by a Monte Carlo scheme, in which the particles combine
a finite propagation speed with a time-step dependent diffusion term. We show
the performance of the method by comparing it with standard (deterministic)
approaches in the literature
Noise reduction in coarse bifurcation analysis of stochastic agent-based models: an example of consumer lock-in
We investigate coarse equilibrium states of a fine-scale, stochastic
agent-based model of consumer lock-in in a duopolistic market. In the model,
agents decide on their next purchase based on a combination of their personal
preference and their neighbours' opinions. For agents with independent
identically-distributed parameters and all-to-all coupling, we derive an
analytic approximate coarse evolution-map for the expected average purchase. We
then study the emergence of coarse fronts when spatial segregation is present
in the relative perceived quality of products. We develop a novel Newton-Krylov
method that is able to compute accurately and efficiently coarse fixed points
when the underlying fine-scale dynamics is stochastic. The main novelty of the
algorithm is in the elimination of the noise that is generated when estimating
Jacobian-vector products using time-integration of perturbed initial
conditions. We present numerical results that demonstrate the convergence
properties of the numerical method, and use the method to show that macroscopic
fronts in this model destabilise at a coarse symmetry-breaking bifurcation.Comment: This version of the manuscript was accepted for publication on SIAD
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