Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guided Probabilistic Simulation of Complex Systems Toward Rare and
  Extreme Events   Simulation based or dynamic probabilistic risk assessment methodologies were
primarily developed for proving a more realistic and complete representation of
complex systems accident response. Such simulation based methodologies have
proven to be particularly powerful for systems with control loops and complex
interactions between its elements, be they hardware, software, or human, as
they provide a natural probabilistic environment to include physical models of
system behavior (e.g., coupled neutronics and thermal hydraulic codes for
nuclear power plants), mechanistic models of materials or hardware systems to
predict failure, and those of natural hazards. Despite the advancements in
simulation based methodologies, the fundamental challenge still persists as the
space of possible probabilistic system trajectories is nearly infinite in size
in simulating even systems of relatively low complexity. In this paper, a
framework is developed to identify rare and extreme events and enabling the use
of reverse trajectories to trace failures (or other system states) to causes
for potential mitigation actions. This framework consists of an Intelligent
Guidance module, Trajectory Generation module and Physical Simulation module.
The Intelligent Guidance module provides planning information to the Trajectory
Generation module that creates scenarios by interacting with the Physical
Simulation in its environment. In turn, system trajectories or scenarios are
created and post processed to provide updating information to the Intelligent
Guidance module or aggregate the results when stopping criteria are met. The
objective of guided simulation is to control the growth of the scenario tree
and to efficiently identify important scenarios that meet single or multiple
criteria. We present several solution strategies, both qualitative and data
driven for each module.

34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org