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We design a particle interpretation of Feynman-Kac measures on path spaces
based on a backward Markovian representation combined with a traditional
mean field particle interpretation of the flow of their final time
marginals. In contrast to traditional genealogical tree based models, these
new particle algorithms can be used to compute normalized additive
functionals “on-the-fly” as well as their
limiting occupation measures with a given precision degree that does not
depend on the final time horizon.
We...
We present a nonasymptotic theorem for interacting particle approximations of unnormalized Feynman–Kac models. We provide an original stochastic analysis-based on Feynman–Kac semigroup techniques combined with recently developed coalescent tree-based functional representations of particle block distributions. We present some regularity conditions under which the -relative error of these weighted particle measures grows linearly with respect to the time horizon yielding what seems to be the first...
The dynamics of dendritic growth of a crystal in an undercooled melt is
determined by macroscopic diffusion-convection of heat and by capillary forces
acting on the nanometer scale of the solid-liquid interface width.
Its modelling is useful for instance in processing techniques based on casting.
The phase-field method is widely used to study evolution of such microstructural phase transformations on
a continuum level; it couples the energy equation to a phenomenological Allen-Cahn/Ginzburg-Landau
equation...
We are interested in the approximation and simulation of solutions for the backward stochastic differential equations. We suggest two approximation schemes, and we study the induced error.
The Diffusion Monte Carlo method is devoted to the computation of
electronic ground-state energies of molecules. In this paper, we focus on
implementations of this method which consist in exploring the
configuration space with a fixed number of random walkers evolving
according to a stochastic differential equation discretized in time. We
allow stochastic reconfigurations of the walkers to reduce the
discrepancy between the weights that they carry. On a simple
one-dimensional example, we prove...
We study a free energy computation procedure, introduced in
[Darve and Pohorille,
J. Chem. Phys.115 (2001) 9169–9183; Hénin and Chipot,
J. Chem. Phys.121 (2004) 2904–2914], which relies on the long-time
behavior of a nonlinear stochastic
differential equation. This nonlinearity comes from a conditional
expectation computed with respect to one coordinate of the solution. The long-time convergence of the solutions to
this equation has been proved
in [Lelièvre et al.,
Nonlinearity21 (2008) 1155–1181],...
To filter perturbed local measurements on a random medium, a dynamic model jointly with an observation transfer equation are needed. Some media given by PDE could have a local probabilistic representation by a Lagrangian stochastic process with mean-field interactions. In this case, we define the acquisition process of locally homogeneous medium along a random path by a Lagrangian Markov process conditioned to be in a domain following the path and conditioned to the observations. The nonlinear...
This paper considers Schrödinger operators, and presents a probabilistic interpretation of the variation (or shape derivative) of the Dirichlet groundstate energy when the associated domain is perturbed. This interpretation relies on the distribution on the boundary of a stopped random process with Feynman-Kac weights. Practical computations require in addition the explicit approximation of the normal derivative of the groundstate on the boundary. We then propose to use this formulation in the...
The paper deals with kernel density estimates of filtering densities in the particle filter. The convergence of the estimates is investigated by means of Fourier analysis. It is shown that the estimates converge to the theoretical filtering densities in the mean integrated squared error. An upper bound on the convergence rate is given. The result is provided under a certain assumption on the Sobolev character of the filtering densities. A sufficient condition is presented for the persistence of...
We consider a stochastic system of particles, usually called vortices in that setting, approximating the 2D Navier-Stokes equation written in vorticity. Assuming that the initial distribution of the position and circulation of the vortices has finite (partial) entropy and a finite moment of positive order, we show that the empirical measure of the particle system converges in law to the unique (under suitable a priori estimates) solution of the 2D Navier-Stokes equation. We actually prove a slightly...
This work aims at introducing modelling, theoretical and numerical studies related to a new downscaling technique applied to computational fluid dynamics.
Our method consists in building a local model, forced by large scale information computed thanks to a classical numerical weather predictor.
The local model, compatible with the Navier-Stokes equations, is used
for the small scale computation (downscaling) of the considered
fluid. It is
inspired by Pope's works on turbulence, and consists in...
We consider a Vlasov-Fokker-Planck equation governing the evolution
of the density of interacting and diffusive matter in the space of
positions and velocities.
We use a probabilistic interpretation to obtain convergence towards
equilibrium in Wasserstein distance with an explicit exponential
rate. We also prove a propagation of chaos property for an
associated particle system, and give rates on the approximation of
the solution by the particle system. Finally, a transportation
inequality...
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