Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data
  Assimilation   The data assimilation procedures used in many operational numerical weather
forecasting systems are based around variants of the 4D-Var algorithm. The cost
of solving the 4D-Var problem is dominated by the cost of forward and adjoint
evaluations of the physical model. This motivates their substitution by fast,
approximate surrogate models. Neural networks offer a promising approach for
the data-driven creation of surrogate models. The accuracy of the surrogate
4D-Var problem's solution has been shown to depend explicitly on accurate
modeling of the forward and adjoint for other surrogate modeling approaches and
in the general nonlinear setting. We formulate and analyze several approaches
to incorporating derivative information into the construction of neural network
surrogates. The resulting networks are tested on out of training set data and
in a sequential data assimilation setting on the Lorenz-63 system. Two methods
demonstrate superior performance when compared with a surrogate network trained
without adjoint information, showing the benefit of incorporating adjoint
information into the training process.

8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga