Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HypoSVI: Hypocenter inversion with Stein variational inference and
  Physics Informed Neural Networks   We introduce a scheme for probabilistic hypocenter inversion with Stein
variational inference. Our approach uses a differentiable forward model in the
form of a physics-informed neural network, which we train to solve the Eikonal
equation. This allows for rapid approximation of the posterior by iteratively
optimizing a collection of particles against a kernelized Stein discrepancy. We
show that the method is well-equipped to handle highly non-convex posterior
distributions, which are common in hypocentral inverse problems. A suite of
experiments is performed to examine the influence of the various
hyperparameters. Once trained, the method is valid for any network geometry
within the study area without the need to build travel time tables. We show
that the computational demands scale efficiently with the number of
differential times, making it ideal for large-N sensing technologies like
Distributed Acoustic Sensing.

24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
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
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex