[Comp-neuro] Frontiers Research Topic on Data Assimilation

Axel Hutt digitalesbad at gmail.com
Mon Aug 27 07:05:14 CEST 2018


Announcement of the Frontiers Research Topic

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Data Assimilation of Nonlocal Observations in Complex Systems
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Natural complex systems exhibit spatio-temporal dynamics on multiple scales
that are difficult to predict and understand. To gain deeper insights into
the system's dynamics and to be able to predict its evolution, observations
of that system are analyzed which allows to derive or motivate models that
fit that system. In general, one may distinguish two types of data. The
so-called in-situ or local observations capture direct measurements of the
system itself, such as temperature at a specific height in the atmosphere
or electric potentials in biological cells. Other observations are not
measured at a certain location but represent the integral of some relevant
quantities manifesting the system's activity. Examples in meteorology for
such nonlocal observations are satellite radiances, slant delays and radio
occultation based on GPS data, or radar reflectivities. In biological
systems, the non-invasive measurement techniques provide nonlocal
observations, such as electro- and magnetoencephalogram or Magnetic
Resonance Imaging. In addition to observations, realistic models are
essential to improve the understanding of natural complex systems and to
predict their dynamical evolution.

To merge both models and observations, it is essential to develop
techniques that optimally estimate the system activity well-adapted to a
model and observed data. Data assimilation comprises a number of methods to
merge diverse experimental data with an underlying model. Data assimilation
optimally combines observations and a model to achieve a certain goal, such
as optimal fitting of model parameters or providing optimal forecasts of
the system's dynamics. Since the recent years have shown an increasing
number of observation techniques capturing integrals of system activity,
data assimilation of nonlocal observations becomes more and more important.

The present Research Topic aims to bring together recent theoretical work
in data assimilation of nonlocal observations with a strong link to
specific applications. This article collection reflects the
state-of-the-art in this research field. Examples of theoretical topics (as
an unconstrained open list) are Kalman filters, variational assimilation
techniques, regression techniques and stochastic optimization techniques.
Applications may range from the parameter estimation in genetic regulatory
networks over prediction of brain dynamics to weather forecast.

We invite you to submit an abstract until November 30, 2018 and the
manuscript until June 30, 2019.  Contributions will be published as soon as
they are accepted and synchronously gathered in the Research Topic volume.

For more information, do not hesitate to contact us (email of Axel Hutt:
digitalesbad at gmail.com).

The organisers
Lili Lei (Nanjing University)
Marc Bocquet (Ecole des Ponts ParisTech)
Alberto Carrassi (Nansen Environmental Remote Sensing Center)
Axel Hutt (Deutscher Wetterdienst)
Roland Potthast (Deutscher Wetterdienst)


-- 
Axel Hutt
Directeur de Recherche
Deutscher Wetterdienst - German Meteorological Service
Research and Development, Department FE 12 (Data Assimilation)
Frankfurter Strasse 135, 63067 Offenbach, Germany Tel.: +49 69 8062 2750
http://www.geocities.ws/digitalbath/
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