26,071 research outputs found
Efficient white noise sampling and coupling for multilevel Monte Carlo with non-nested meshes
When solving stochastic partial differential equations (SPDEs) driven by
additive spatial white noise, the efficient sampling of white noise
realizations can be challenging. Here, we present a new sampling technique that
can be used to efficiently compute white noise samples in a finite element
method and multilevel Monte Carlo (MLMC) setting. The key idea is to exploit
the finite element matrix assembly procedure and factorize each local mass
matrix independently, hence avoiding the factorization of a large matrix.
Moreover, in a MLMC framework, the white noise samples must be coupled between
subsequent levels. We show how our technique can be used to enforce this
coupling even in the case of non-nested mesh hierarchies. We demonstrate the
efficacy of our method with numerical experiments. We observe optimal
convergence rates for the finite element solution of the elliptic SPDEs of
interest in 2D and 3D and we show convergence of the sampled field covariances.
In a MLMC setting, a good coupling is enforced and the telescoping sum is
respected.Comment: 28 pages, 10 figure
Magnetometry via a double-pass continuous quantum measurement of atomic spin
We argue that it is possible in principle to reduce the uncertainty of an
atomic magnetometer by double-passing a far-detuned laser field through the
atomic sample as it undergoes Larmor precession. Numerical simulations of the
quantum Fisher information suggest that, despite the lack of explicit
multi-body coupling terms in the system's magnetic Hamiltonian, the parameter
estimation uncertainty in such a physical setup scales better than the
conventional Heisenberg uncertainty limit over a specified but arbitrary range
of particle number N. Using the methods of quantum stochastic calculus and
filtering theory, we demonstrate numerically an explicit parameter estimator
(called a quantum particle filter) whose observed scaling follows that of our
calculated quantum Fisher information. Moreover, the quantum particle filter
quantitatively surpasses the uncertainty limit calculated from the quantum
Cramer-Rao inequality based on a magnetic coupling Hamiltonian with only
single-body operators. We also show that a quantum Kalman filter is
insufficient to obtain super-Heisenberg scaling, and present evidence that such
scaling necessitates going beyond the manifold of Gaussian atomic states.Comment: 17 pages, updated to match print versio
Quantum projection filter for a highly nonlinear model in cavity QED
Both in classical and quantum stochastic control theory a major role is
played by the filtering equation, which recursively updates the information
state of the system under observation. Unfortunately, the theory is plagued by
infinite-dimensionality of the information state which severely limits its
practical applicability, except in a few select cases (e.g. the linear Gaussian
case.) One solution proposed in classical filtering theory is that of the
projection filter. In this scheme, the filter is constrained to evolve in a
finite-dimensional family of densities through orthogonal projection on the
tangent space with respect to the Fisher metric. Here we apply this approach to
the simple but highly nonlinear quantum model of optical phase bistability of a
stongly coupled two-level atom in an optical cavity. We observe near-optimal
performance of the quantum projection filter, demonstrating the utility of such
an approach.Comment: 19 pages, 6 figures. A version with high quality images can be found
at http://minty.caltech.edu/papers.ph
Simulation of multivariate diffusion bridge
We propose simple methods for multivariate diffusion bridge simulation, which
plays a fundamental role in simulation-based likelihood and Bayesian inference
for stochastic differential equations. By a novel application of classical
coupling methods, the new approach generalizes a previously proposed simulation
method for one-dimensional bridges to the multi-variate setting. First a method
of simulating approximate, but often very accurate, diffusion bridges is
proposed. These approximate bridges are used as proposal for easily
implementable MCMC algorithms that produce exact diffusion bridges. The new
method is much more generally applicable than previous methods. Another
advantage is that the new method works well for diffusion bridges in long
intervals because the computational complexity of the method is linear in the
length of the interval. In a simulation study the new method performs well, and
its usefulness is illustrated by an application to Bayesian estimation for the
multivariate hyperbolic diffusion model.Comment: arXiv admin note: text overlap with arXiv:1403.176
Parameter Estimation for the Stochastically Perturbed Navier-Stokes Equations
We consider a parameter estimation problem to determine the viscosity
of a stochastically perturbed 2D Navier-Stokes system. We derive several
different classes of estimators based on the first Fourier modes of a
single sample path observed on a finite time interval. We study the consistency
and asymptotic normality of these estimators. Our analysis treats strong,
pathwise solutions for both the periodic and bounded domain cases in the
presence of an additive white (in time) noise.Comment: to appear in SP
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