9,081 research outputs found
Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation
This paper presents an approach for employing artificial neural networks (NN)
to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation.
The assimilation methods are tested in the Simplified Parameterizations
PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation
model (AGCM), using synthetic observational data simulating localization of
balloon soundings. For the data assimilation scheme, the supervised NN, the
multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the
analysis from the local ensemble transform Kalman filter (LETKF). After the
training process, the method using the MLP-NN is seen as a function of data
assimilation. The NN were trained with data from first three months of 1982,
1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle
using MLP-NN were performed with synthetic observations for January 1985. The
numerical results demonstrate the effectiveness of the NN technique for
atmospheric data assimilation. The results of the NN analyses are very close to
the results from the LETKF analyses, the differences of the monthly average of
absolute temperature analyses is of order 0.02. The simulations show that the
major advantage of using the MLP-NN is better computational performance, since
the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN
is 90 times faster than cycle assimilation with LETKF for the numerical
experiment.Comment: 17 pages, 16 figures, monthly weather revie
Ensemble Kalman filter for neural network based one-shot inversion
We study the use of novel techniques arising in machine learning for inverse
problems. Our approach replaces the complex forward model by a neural network,
which is trained simultaneously in a one-shot sense when estimating the unknown
parameters from data, i.e. the neural network is trained only for the unknown
parameter. By establishing a link to the Bayesian approach to inverse problems,
an algorithmic framework is developed which ensures the feasibility of the
parameter estimate w.r. to the forward model. We propose an efficient,
derivative-free optimization method based on variants of the ensemble Kalman
inversion. Numerical experiments show that the ensemble Kalman filter for
neural network based one-shot inversion is a promising direction combining
optimization and machine learning techniques for inverse problems
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection
Long Short-Term Memory networks trained with gradient descent and
back-propagation have received great success in various applications. However,
point estimation of the weights of the networks is prone to over-fitting
problems and lacks important uncertainty information associated with the
estimation. However, exact Bayesian neural network methods are intractable and
non-applicable for real-world applications. In this study, we propose an
approximate estimation of the weights uncertainty using Ensemble Kalman Filter,
which is easily scalable to a large number of weights. Furthermore, we optimize
the covariance of the noise distribution in the ensemble update step using
maximum likelihood estimation. To assess the proposed algorithm, we apply it to
outlier detection in five real-world events retrieved from the Twitter
platform
Reionization history constraints from neural network based predictions of high-redshift quasar continua
Observations of the early Universe suggest that reionization was complete by
, however, the exact history of this process is still unknown. One
method for measuring the evolution of the neutral fraction throughout this
epoch is via observing the Ly damping wings of high-redshift quasars.
In order to constrain the neutral fraction from quasar observations, one needs
an accurate model of the quasar spectrum around Ly, after the spectrum
has been processed by its host galaxy but before it is altered by absorption
and damping in the intervening IGM. In this paper, we present a novel machine
learning approach, using artificial neural networks, to reconstruct quasar
continua around Ly. Our QSANNdRA algorithm improves the error in this
reconstruction compared to the state-of-the-art PCA-based model in the
literature by 14.2% on average, and provides an improvement of 6.1% on average
when compared to an extension thereof. In comparison with the extended PCA
model, QSANNdRA further achieves an improvement of 22.1% and 16.8% when
evaluated on low-redshift quasars most similar to the two high-redshift quasars
under consideration, ULAS J1120+0641 at and ULAS J1342+0928 at
, respectively. Using our more accurate reconstructions of these two
quasars, we estimate the neutral fraction of the IGM using a homogeneous
reionization model and find at
and at . Our
results are consistent with the literature and favour a rapid end to
reionization
Stellar classification from single-band imaging using machine learning
Information on the spectral types of stars is of great interest in view of
the exploitation of space-based imaging surveys. In this article, we
investigate the classification of stars into spectral types using only the
shape of their diffraction pattern in a single broad-band image. We propose a
supervised machine learning approach to this endeavour, based on principal
component analysis (PCA) for dimensionality reduction, followed by artificial
neural networks (ANNs) estimating the spectral type. Our analysis is performed
with image simulations mimicking the Hubble Space Telescope (HST) Advanced
Camera for Surveys (ACS) in the F606W and F814W bands, as well as the Euclid
VIS imager. We first demonstrate this classification in a simple context,
assuming perfect knowledge of the point spread function (PSF) model and the
possibility of accurately generating mock training data for the machine
learning. We then analyse its performance in a fully data-driven situation, in
which the training would be performed with a limited subset of bright stars
from a survey, and an unknown PSF with spatial variations across the detector.
We use simulations of main-sequence stars with flat distributions in spectral
type and in signal-to-noise ratio, and classify these stars into 13 spectral
subclasses, from O5 to M5. Under these conditions, the algorithm achieves a
high success rate both for Euclid and HST images, with typical errors of half a
spectral class. Although more detailed simulations would be needed to assess
the performance of the algorithm on a specific survey, this shows that stellar
classification from single-band images is well possible.Comment: 10 pages, 9 figures, 2 tables, accepted in A&
Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly available.
Parallel to this is the need for new methods of reconstructing (inferring) the
topology of networks from time-resolved observations of their node-dynamics.
The methods based on physical insights often rely on strong assumptions about
the properties and dynamics of the scrutinized network. Here, we use the
insights from machine learning to design a new method of network reconstruction
that essentially makes no such assumptions. Specifically, we interpret the
available trajectories (data) as features, and use two independent feature
ranking approaches -- Random forest and RReliefF -- to rank the importance of
each node for predicting the value of each other node, which yields the
reconstructed adjacency matrix. We show that our method is fairly robust to
coupling strength, system size, trajectory length and noise. We also find that
the reconstruction quality strongly depends on the dynamical regime
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