Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying stochastic governing equations from data of the most
  probable transition trajectories   Extracting governing stochastic differential equation models from elusive
data is crucial to understand and forecast dynamics for complex systems. We
devise a method to extract the drift term and estimate the diffusion
coefficient of a governing stochastic dynamical system, from its time-series
data of the most probable transition trajectory. By the Onsager-Machlup theory,
the most probable transition trajectory satisfies the corresponding
Euler-Lagrange equation, which is a second order deterministic ordinary
differential equation involving the drift term and diffusion coefficient. We
first estimate the coefficients of the Euler-Lagrange equation based on the
data of the most probable trajectory, and then we calculate the drift and
diffusion coefficients of the governing stochastic dynamical system. These two
steps involve sparse regression and optimization. Finally, we illustrate our
method with an example and some discussions.

37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat