Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A DNN Framework for Learning Lagrangian Drift With Uncertainty   Reconstructions of Lagrangian drift, for example for objects lost at sea, are
often uncertain due to unresolved physical phenomena within the data.
Uncertainty is usually overcome by introducing stochasticity into the drift,
but this approach requires specific assumptions for modelling uncertainty. We
remove this constraint by presenting a purely data-driven framework for
modelling probabilistic drift in flexible environments. We train a CNN to
predict the temporal evolution of probability density maps of particle
locations from $t$ to $t+1$ given an input velocity field. We generate
groundtruth density maps on the basis of ocean circulation model simulations by
simulating uncertainty in the initial position of particle trajectories.
Several loss functions for regressing the predicted density maps are tested.
Through evaluating our model on unseen velocities from a different year, we
find its outputs to be in good agreement with numerical simulations, suggesting
satisfactory generalisation to different dynamical situations.

4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: stat
24
The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph