14,394 research outputs found
Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results
We propose a new algorithm for an adaptive optics system control law, based
on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with
localizations. It allows to handle non-stationary behaviors, to obtain
performance close to the optimality defined with the residual phase variance
minimization criterion, and to reduce the computational burden with an
intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).Comment: This paper was published in Optics Express and is made available as
an electronic reprint with the permission of OSA. The paper can be found at
the following URL on the OSA website: http://www.opticsinfobase.org/oe/ .
Systematic or multiple reproduction or distribution to multiple locations via
electronic or other means is prohibited and is subject to penalties under la
Initial Conditions for Large Cosmological Simulations
This technical paper describes a software package that was designed to
produce initial conditions for large cosmological simulations in the context of
the Horizon collaboration. These tools generalize E. Bertschinger's Grafic1
software to distributed parallel architectures and offer a flexible alternative
to the Grafic2 software for ``zoom'' initial conditions, at the price of large
cumulated cpu and memory usage. The codes have been validated up to resolutions
of 4096^3 and were used to generate the initial conditions of large
hydrodynamical and dark matter simulations. They also provide means to generate
constrained realisations for the purpose of generating initial conditions
compatible with, e.g. the local group, or the SDSS catalog.Comment: 12 pages, 11 figures, submitted to ApJ
Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy
In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation
Implicit particle methods and their connection with variational data assimilation
The implicit particle filter is a sequential Monte Carlo method for data
assimilation that guides the particles to the high-probability regions via a
sequence of steps that includes minimizations. We present a new and more
general derivation of this approach and extend the method to particle smoothing
as well as to data assimilation for perfect models. We show that the
minimizations required by implicit particle methods are similar to the ones one
encounters in variational data assimilation and explore the connection of
implicit particle methods with variational data assimilation. In particular, we
argue that existing variational codes can be converted into implicit particle
methods at a low cost, often yielding better estimates, that are also equipped
with quantitative measures of the uncertainty. A detailed example is presented
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
We introduce a data-driven forecasting method for high-dimensional chaotic
systems using long short-term memory (LSTM) recurrent neural networks. The
proposed LSTM neural networks perform inference of high-dimensional dynamical
systems in their reduced order space and are shown to be an effective set of
nonlinear approximators of their attractor. We demonstrate the forecasting
performance of the LSTM and compare it with Gaussian processes (GPs) in time
series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation
and a prototype climate model. The LSTM networks outperform the GPs in
short-term forecasting accuracy in all applications considered. A hybrid
architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is
proposed to ensure convergence to the invariant measure. This novel hybrid
method is fully data-driven and extends the forecasting capabilities of LSTM
networks.Comment: 31 page
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