49,161 research outputs found
An Analysis of Publication Venues for Automatic Differentiation Research
We present the results of our analysis of publication venues for papers on
automatic differentiation (AD), covering academic journals and conference
proceedings. Our data are collected from the AD publications database
maintained by the autodiff.org community website. The database is purpose-built
for the AD field and is expanding via submissions by AD researchers. Therefore,
it provides a relatively noise-free list of publications relating to the field.
However, it does include noise in the form of variant spellings of journal and
conference names. We handle this by manually correcting and merging these
variants under the official names of corresponding venues. We also share the
raw data we get after these corrections.Comment: 6 pages, 3 figure
A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. I: Methodology
Data assimilation obtains improved estimates of the state of a physical system
by combining imperfect model results with sparse and noisy observations of reality.
Not all observations used in data assimilation are equally valuable. The ability to
characterize the usefulness of different data points is important for analyzing the
effectiveness of the assimilation system, for data pruning, and for the design of future
sensor systems.
This paper focuses on the four dimensional variational (4D-Var) data assimilation
framework. Metrics from information theory are used to quantify the contribution
of observations to decreasing the uncertainty with which the system state is known.
We establish an interesting relationship between different information-theoretic metrics
and the variational cost function/gradient under Gaussian linear assumptions.
Based on this insight we derive an ensemble-based computational procedure to estimate
the information content of various observations in the context of 4D-Var. The
approach is illustrated on linear and nonlinear test problems. In the companion paper
[Singh et al.(2011)] the methodology is applied to a global chemical data assimilation
problem
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which
originate in processes that need to be parameterized, such as clouds,
convection, and ecosystems. But rapid progress is now within reach. New
computational tools and methods from data assimilation and machine learning
make it possible to integrate global observations and local high-resolution
simulations in an Earth system model (ESM) that systematically learns from
both. Here we propose a blueprint for such an ESM. We outline how
parameterization schemes can learn from global observations and targeted
high-resolution simulations, for example, of clouds and convection, through
matching low-order statistics between ESMs, observations, and high-resolution
simulations. We illustrate learning algorithms for ESMs with a simple dynamical
system that shares characteristics of the climate system; and we discuss the
opportunities the proposed framework presents and the challenges that remain to
realize it.Comment: 32 pages, 3 figure
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