Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of Deep Learning-based Interpolation Methods to Nearshore
  Bathymetry   Nearshore bathymetry, the topography of the ocean floor in coastal zones, is
vital for predicting the surf zone hydrodynamics and for route planning to
avoid subsurface features. Hence, it is increasingly important for a wide
variety of applications, including shipping operations, coastal management, and
risk assessment. However, direct high resolution surveys of nearshore
bathymetry are rarely performed due to budget constraints and logistical
restrictions. Another option when only sparse observations are available is to
use Gaussian Process regression (GPR), also called Kriging. But GPR has
difficulties recognizing patterns with sharp gradients, like those found around
sand bars and submerged objects, especially when observations are sparse. In
this work, we present several deep learning-based techniques to estimate
nearshore bathymetry with sparse, multi-scale measurements. We propose a Deep
Neural Network (DNN) to compute posterior estimates of the nearshore
bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that
samples from the posterior distribution. We train our neural networks based on
synthetic data generated from nearshore surveys provided by the U.S.\ Army
Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We
compare our methods with Kriging on real surveys as well as surveys with
artificially added sharp gradients. Results show that direct estimation by DNN
gives better predictions than Kriging in this application. We use bootstrapping
with DNN for uncertainty quantification. We also propose a method, named
DNN-Kriging, that combines deep learning with Kriging and shows further
improvement of the posterior estimates.

17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph