39 research outputs found
An Ensemble Kalman-Particle Predictor-Corrector Filter for Non-Gaussian Data Assimilation
An Ensemble Kalman Filter (EnKF, the predictor) is used make a large change
in the state, followed by a Particle Filer (PF, the corrector) which assigns
importance weights to describe non-Gaussian distribution. The weights are
obtained by nonparametric density estimation. It is demonstrated on several
numerical examples that the new predictor-corrector filter combines the
advantages of the EnKF and the PF and that it is suitable for high dimensional
states which are discretizations of solutions of partial differential
equations.Comment: ICCS 2009, to appear; 9 pages; minor edit
FooPar: A Functional Object Oriented Parallel Framework in Scala
We present FooPar, an extension for highly efficient Parallel Computing in
the multi-paradigm programming language Scala. Scala offers concise and clean
syntax and integrates functional programming features. Our framework FooPar
combines these features with parallel computing techniques. FooPar is designed
modular and supports easy access to different communication backends for
distributed memory architectures as well as high performance math libraries. In
this article we use it to parallelize matrix matrix multiplication and show its
scalability by a isoefficiency analysis. In addition, results based on a
empirical analysis on two supercomputers are given. We achieve close-to-optimal
performance wrt. theoretical peak performance. Based on this result we conclude
that FooPar allows to fully access Scala's design features without suffering
from performance drops when compared to implementations purely based on C and
MPI
Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter
Agent-based modelling (ABM) is ideally suited to simulating crowds of people as it captures the complex behaviours and interactions between individuals that lead to the emergence of crowding. Currently, it is not possible to use ABM for real-time simulation due to the absence of established mechanisms for dynamically incorporating real-time data. This means that, although models are able to perform useful offline crowd simulations, they are unable to simulate the behaviours of crowds in real time. This paper begins to address this drawback by demonstrating how a data assimilation algorithm, the Unscented Kalman Filter (UKF), can be used to incorporate pseudo-real data into an agent-based model at run time. Experiments are conducted to test how well the algorithm works when a proportion of agents are tracked directly under varying levels of uncertainty. Notably, the experiments show that the behaviour of unobserved agents can be inferred from the behaviours of those that are observed. This has implications for modelling real crowds where full knowledge of all individuals will never be known. In presenting a new approach for creating real-time simulations of crowds, this paper has important implications for the management of various environments in global cities, from single buildings to larger structures such as transportation hubs, sports stadiums, through to entire city regions
Manifesto of computational social science
The increasing integration of technology into our lives has created unprecedented volumes of data on society's everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.The publication of this work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 284709, a Coordination and Support Action in the Information and Communication Technologies activity area (‘FuturICT’ FET Flagship Pilot Project). We are grateful to the anonymous reviewers for the insightful comments.Publicad
Enhancing discrete-event simulation with big data analytics: a review
This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques