Optimal Control of the Keilson-Storer Master Equation in a Monte Carlo Framework

2021 ◽  
pp. 1-29
Author(s):  
Jan Bartsch ◽  
Giovanni Nastasi ◽  
Alfio Borzì
Keyword(s):  
Optimal Control ◽  
Monte Carlo ◽  
Master Equation ◽  
Monte Carlo Framework

Coarse-grained modeling of the nucleation of polycyclic aromatic hydrocarbons into soot precursors

2019 ◽  
Vol 21 (9) ◽  
pp. 5123-5132 ◽  
Author(s):  
J. Hernández-Rojas ◽  
F. Calvo
Keyword(s):  
Monte Carlo ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Physical Growth ◽  
Coarse Grained ◽  
Soot Precursors ◽  
Polycyclic Aromatic ◽  
Hydrocarbon Molecules ◽  
A Minor ◽  
Monte Carlo Framework

The aggregation and physical growth of polycyclic aromatic hydrocarbon molecules was simulated using a coarse-grained potential and a stochastic Monte Carlo framework. In agreement with earlier studies, homomolecular nucleation of pyrene, coronene and circumcoronene is found to be limited at temperatures in the 500–1000 K range. Heteromolecular nucleation is found to occur with a minor spontaneous segregation toward pure and equi concentrations.

RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
R Abbassi ◽  
F Khan ◽  
N Khakzad ◽  
B Veitch ◽  
S Ehlers
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Historical Data ◽  
Fault Trees ◽  
Northern Sea Route ◽  
Accident Scenarios ◽  
Oil Tanker ◽  
Harsh Conditions ◽  
Monte Carlo Framework ◽  
Transportation Accident

A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.

Sequential Monte Carlo methods for diffusion processes

2009 ◽  
Vol 465 (2112) ◽  
pp. 3709-3727 ◽  
Author(s):  
Ajay Jasra ◽  
Arnaud Doucet
Keyword(s):  
Optimal Control ◽  
Monte Carlo ◽  
Option Pricing ◽  
Monte Carlo Methods ◽  
Sequential Monte Carlo ◽  
Diffusion Processes ◽  
Time Discretization ◽  
Initial Condition ◽  
Sequential Monte Carlo Methods ◽  
Low Dimensional

In this paper, we show how to use sequential Monte Carlo methods to compute expectations of functionals of diffusions at a given time and the gradients of these quantities w.r.t. the initial condition of the process. In some cases, via the exact simulation of the diffusion, there is no time discretization error, otherwise the methods use Euler discretization. We illustrate our approach on both high- and low-dimensional problems from optimal control and establish that our approach substantially outperforms standard Monte Carlo methods typically adopted in the literature. The methods developed here are appropriate for solving a certain class of partial differential equations as well as for option pricing and hedging.

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