17,237 research outputs found
Analysis of the 3DVAR Filter for the Partially Observed Lorenz '63 Model
The problem of effectively combining data with a mathematical model
constitutes a major challenge in applied mathematics. It is particular
challenging for high-dimensional dynamical systems where data is received
sequentially in time and the objective is to estimate the system state in an
on-line fashion; this situation arises, for example, in weather forecasting.
The sequential particle filter is then impractical and ad hoc filters, which
employ some form of Gaussian approximation, are widely used. Prototypical of
these ad hoc filters is the 3DVAR method. The goal of this paper is to analyze
the 3DVAR method, using the Lorenz '63 model to exemplify the key ideas. The
situation where the data is partial and noisy is studied, and both discrete
time and continuous time data streams are considered. The theory demonstrates
how the widely used technique of variance inflation acts to stabilize the
filter, and hence leads to asymptotic accuracy
Data Assimilation: A Mathematical Introduction
These notes provide a systematic mathematical treatment of the subject of
data assimilation
Determining White Noise Forcing From Eulerian Observations in the Navier Stokes Equation
The Bayesian approach to inverse problems is of paramount importance in
quantifying uncertainty about the input to and the state of a system of
interest given noisy observations. Herein we consider the forward problem of
the forced 2D Navier Stokes equation. The inverse problem is inference of the
forcing, and possibly the initial condition, given noisy observations of the
velocity field. We place a prior on the forcing which is in the form of a
spatially correlated temporally white Gaussian process, and formulate the
inverse problem for the posterior distribution. Given appropriate spatial
regularity conditions, we show that the solution is a continuous function of
the forcing. Hence, for appropriately chosen spatial regularity in the prior,
the posterior distribution on the forcing is absolutely continuous with respect
to the prior and is hence well-defined. Furthermore, the posterior distribution
is a continuous function of the data. We complement this theoretical result
with numerical simulation of the posterior distribution
Longhorn Cavern, A Reconnaissance Survey
The recent opening of the Longhorn Cavern, twelve miles south of the town of Burnet, in the Central Mineral Region of Texas, as a state park, has given this fine cave known by local residents for almost a hundred years, state and national interest
Well-Posedness And Accuracy Of The Ensemble Kalman Filter In Discrete And Continuous Time
The ensemble Kalman filter (EnKF) is a method for combining a dynamical model
with data in a sequential fashion. Despite its widespread use, there has been
little analysis of its theoretical properties. Many of the algorithmic
innovations associated with the filter, which are required to make a useable
algorithm in practice, are derived in an ad hoc fashion. The aim of this paper
is to initiate the development of a systematic analysis of the EnKF, in
particular to do so in the small ensemble size limit. The perspective is to
view the method as a state estimator, and not as an algorithm which
approximates the true filtering distribution. The perturbed observation version
of the algorithm is studied, without and with variance inflation. Without
variance inflation well-posedness of the filter is established; with variance
inflation accuracy of the filter, with resepct to the true signal underlying
the data, is established. The algorithm is considered in discrete time, and
also for a continuous time limit arising when observations are frequent and
subject to large noise. The underlying dynamical model, and assumptions about
it, is sufficiently general to include the Lorenz '63 and '96 models, together
with the incompressible Navier-Stokes equation on a two-dimensional torus. The
analysis is limited to the case of complete observation of the signal with
additive white noise. Numerical results are presented for the Navier-Stokes
equation on a two-dimensional torus for both complete and partial observations
of the signal with additive white noise
Not all the bots are created equal:the Ordering Turing Test for the labelling of bots in MMORPGs
This article contributes to the research on bots in Social Media. It takes as its starting point an emerging perspective which proposes that we should abandon the investigation of the Turing Test and the functional aspects of bots in favor of studying the authentic and cooperative relationship between humans and bots. Contrary to this view, this article argues that Turing Tests are one of the ways in which authentic relationships between humans and bots take place. To understand this, this article introduces the concept of Ordering Turing Tests: these are sort of Turing Tests proposed by social actors for purposes of achieving social order when bots produce deviant behavior. An Ordering Turing Test is method for labeling deviance, whereby social actors can use this test to tell apart rule-abiding humans and rule-breaking bots. Using examples from Massively Multiplayer Online Role-Playing Games, this article illustrates how Ordering Turing Tests are proposed and justified by players and service providers. Data for the research comes from scientific literature on Machine Learning proposed for the identification of bots and from game forums and other player produced paratexts from the case study of the game Runescape
Atomic layer deposition of ZnS nanotubes
We report on growth of high-aspect-ratio () zinc sulfide
nanotubes with variable, precisely tunable, wall thicknesses and tube diameters
into highly ordered pores of anodic alumina templates by atomic layer
deposition (ALD) at temperatures as low as 75 C. Various
characterization techniques are employed to gain information on the
composition, morphology, and crystal structure of the synthesized samples.
Besides practical applications, the ALD-grown tubes could be envisaged as model
systems for the study of a certain class of size-dependent quantum and
classical phenomena.Comment: 1 LaTeX source file, 8 eps figures, and the manuscript in PDF forma
Stability of Filters for the Navier-Stokes Equation
Data assimilation methodologies are designed to incorporate noisy
observations of a physical system into an underlying model in order to infer
the properties of the state of the system. Filters refer to a class of data
assimilation algorithms designed to update the estimation of the state in a
on-line fashion, as data is acquired sequentially. For linear problems subject
to Gaussian noise filtering can be performed exactly using the Kalman filter.
For nonlinear systems it can be approximated in a systematic way by particle
filters. However in high dimensions these particle filtering methods can break
down. Hence, for the large nonlinear systems arising in applications such as
weather forecasting, various ad hoc filters are used, mostly based on making
Gaussian approximations. The purpose of this work is to study the properties of
these ad hoc filters, working in the context of the 2D incompressible
Navier-Stokes equation. By working in this infinite dimensional setting we
provide an analysis which is useful for understanding high dimensional
filtering, and is robust to mesh-refinement. We describe theoretical results
showing that, in the small observational noise limit, the filters can be tuned
to accurately track the signal itself (filter stability), provided the system
is observed in a sufficiently large low dimensional space; roughly speaking
this space should be large enough to contain the unstable modes of the
linearized dynamics. Numerical results are given which illustrate the theory.
In a simplified scenario we also derive, and study numerically, a stochastic
PDE which determines filter stability in the limit of frequent observations,
subject to large observational noise. The positive results herein concerning
filter stability complement recent numerical studies which demonstrate that the
ad hoc filters perform poorly in reproducing statistical variation about the
true signal
Packet narrowing and quantum entanglement in photoionization and photodissociation
The narrowing of electron and ion wave packets in the process of
photoionization is investigated, with the electron-ion recoil fully taken into
account. Packet localization of this type is directly related to entanglement
in the joint quantum state of electron and ion, and to Einstein-Podolsky-Rosen
localization. Experimental observation of such packet-narrowing effects is
suggested via coincidence registration by two detectors, with a fixed position
of one and varying position of the other. A similar effect, typically with an
enhanced degree of entanglement, is shown to occur in the case of
photodissociation of molecules
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