Data Assimilation in Discrete Event Simulations -- A Rollback based Sequential Monte Carlo Approach

2016 ◽  
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Sequential Monte Carlo ◽  
Discrete Event ◽  
Monte Carlo Approach ◽  
Discrete Event Simulations ◽  
Event Simulations

scholarly journals A particle filter-based data assimilation framework for discrete event simulations

SIMULATION ◽  
2018 ◽  
Vol 95 (11) ◽  
pp. 1027-1053 ◽  
Author(s):  
Xu Xie ◽  
Alexander Verbraeck
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Posterior Distribution ◽  
Discrete Event ◽  
Accurate Estimation ◽  
System Specification ◽  
Discrete Event Simulations ◽  
Variable Dimension ◽  
State Trajectory ◽  
Event Simulations

With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event simulations. The framework is formally defined based on the Discrete Event System Specification formalism. To effectively apply particle filtering in discrete event simulations, we introduce an interpolation operation that considers the elapsed time (i.e., the time elapsed since the last state transition) when retrieving the model state (which was ignored in related work) in order to obtain updated state values. The data assimilation problem finally boils down to estimating the posterior distribution of a state trajectory with variable dimension. This seems to be problematic; however, it is proven that in practice we can safely apply the sequential importance sampling algorithm to update the random measure (i.e., a set of particles and their importance weights) that approximates this posterior distribution of the state trajectory with variable dimension. To illustrate the working of the proposed data assimilation framework, a case is studied in a gold mine system to estimate truck arrival times at the bottom of the vertical shaft. The results show that the framework is able to provide accurate estimation results in discrete event simulations; it is also shown that the framework is robust to errors both in the simulation model and in the data.

scholarly journals A sequential Monte Carlo approach to estimate biophysical neural models from spikes

2011 ◽  
Vol 8 (6) ◽  
pp. 065006 ◽  
Author(s):  
Liang Meng ◽  
Mark A Kramer ◽  
Uri T Eden
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Neural Models ◽  
Monte Carlo Approach

scholarly journals Pathway Complexity of Model Virus Capsid Assembly Systems

2008 ◽  
Vol 9 (3-4) ◽  
pp. 277-293 ◽  
Author(s):  
Navodit Misra ◽  
Daniel Lees ◽  
Tiequan Zhang ◽  
Russell Schwartz
Keyword(s):  
Discrete Event ◽  
Reaction Pathways ◽  
Assembly Systems ◽  
Capsid Assembly ◽  
Reaction Systems ◽  
Discrete Event Simulations ◽  
Differential Equation Models ◽  
Assembly Kinetics ◽  
Pathway Models ◽  
Event Simulations

As computational and mathematical studies become increasingly central to studies of complicated reaction systems, it will become ever more important to identify the assumptions our models must make and determine when those assumptions are valid. Here, we examine that question with respect to viral capsid assembly by studying the ‘pathway complexity’ of model capsid assembly systems, which we informally define as the number of reaction pathways and intermediates one must consider to accurately describe a given system. We use two model types for this study: ordinary differential equation models, which allow us to precisely and deterministically compare the accuracy of capsid models under different degrees of simplification, and stochastic discrete event simulations, which allow us to sample use of reaction intermediates across a wide parameter space allowing for an extremely large number of possible reaction pathways. The models provide complementary information in support of a common conclusion that the ability of simple pathway models to adequately explain capsid assembly kinetics varies considerably across the space of biologically meaningful assembly parameters. These studies provide grounds for caution regarding our ability to reliably represent real systems with simple models and to extrapolate results from one set of assembly conditions to another. In addition, the analysis tools developed for this study are likely to have broader use in the analysis and efficient simulation of large reaction systems.

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