Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating uncertainty of earthquake rupture using Bayesian neural
  network   Bayesian neural networks (BNN) are the probabilistic model that combines the
strengths of both neural network (NN) and stochastic processes. As a result,
BNN can combat overfitting and perform well in applications where data is
limited. Earthquake rupture study is such a problem where data is insufficient,
and scientists have to rely on many trial and error numerical or physical
models. Lack of resources and computational expenses, often, it becomes hard to
determine the reasons behind the earthquake rupture. In this work, a BNN has
been used (1) to combat the small data problem and (2) to find out the
parameter combinations responsible for earthquake rupture and (3) to estimate
the uncertainty associated with earthquake rupture. Two thousand rupture
simulations are used to train and test the model. A simple 2D rupture geometry
is considered where the fault has a Gaussian geometric heterogeneity at the
center, and eight parameters vary in each simulation. The test F1-score of BNN
(0.8334), which is 2.34% higher than plain NN score. Results show that the
parameters of rupture propagation have higher uncertainty than the rupture
arrest. Normal stresses play a vital role in determining rupture propagation
and are also the highest source of uncertainty, followed by the dynamic
friction coefficient. Shear stress has a moderate role, whereas the geometric
features such as the width and height of the fault are least significant and
uncertain.

7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th