Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational encoder geostatistical analysis (VEGAS) with an application
  to large scale riverine bathymetry   Estimation of riverbed profiles, also known as bathymetry, plays a vital role
in many applications, such as safe and efficient inland navigation, prediction
of bank erosion, land subsidence, and flood risk management. The high cost and
complex logistics of direct bathymetry surveys, i.e., depth imaging, have
encouraged the use of indirect measurements such as surface flow velocities.
However, estimating high-resolution bathymetry from indirect measurements is an
inverse problem that can be computationally challenging. Here, we propose a
reduced-order model (ROM) based approach that utilizes a variational
autoencoder (VAE), a type of deep neural network with a narrow layer in the
middle, to compress bathymetry and flow velocity information and accelerate
bathymetry inverse problems from flow velocity measurements. In our
application, the shallow-water equations (SWE) with appropriate boundary
conditions (BCs), e.g., the discharge and/or the free surface elevation,
constitute the forward problem, to predict flow velocity. Then, ROMs of the
SWEs are constructed on a nonlinear manifold of low dimensionality through a
variational encoder. Estimation with uncertainty quantification (UQ) is
performed on the low-dimensional latent space in a Bayesian setting. We have
tested our inversion approach on a one-mile reach of the Savannah River, GA,
USA. Once the neural network is trained (offline stage), the proposed technique
can perform the inversion operation orders of magnitude faster than traditional
inversion methods that are commonly based on linear projections, such as
principal component analysis (PCA), or the principal component geostatistical
approach (PCGA). Furthermore, tests show that the algorithm can estimate the
bathymetry with good accuracy even with sparse flow velocity measurements.

19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th