Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Variational Data Assimilation Models and Solvers   This paper addresses variational data assimilation from a learning point of
view. Data assimilation aims to reconstruct the time evolution of some state
given a series of observations, possibly noisy and irregularly-sampled. Using
automatic differentiation tools embedded in deep learning frameworks, we
introduce end-to-end neural network architectures for data assimilation. It
comprises two key components: a variational model and a gradient-based solver
both implemented as neural networks. A key feature of the proposed end-to-end
learning architecture is that we may train the NN models using both supervised
and unsupervised strategies. Our numerical experiments on Lorenz-63 and
Lorenz-96 systems report significant gain w.r.t. a classic gradient-based
minimization of the variational cost both in terms of reconstruction
performance and optimization complexity. Intriguingly, we also show that the
variational models issued from the true Lorenz-63 and Lorenz-96 ODE
representations may not lead to the best reconstruction performance. We believe
these results may open new research avenues for the specification of
assimilation models in geoscience.

11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph