Search Results for sequential-monte-carlo-framework-for-dynamic-data-driven-event-reconstruction-for-atmospheric-release

The release of hazardous materials into the atmosphere can have a tremendous impact on dense populations.

Author: J. Lundquist

Publisher:

ISBN: OCLC:316305392

Category:

Page: 6

View: 426

DOWNLOAD & READ
The release of hazardous materials into the atmosphere can have a tremendous impact on dense populations. We propose an atmospheric event reconstruction framework that couples observed data and predictive computer-intensive dispersion models via Bayesian methodology. Due to the complexity of the model framework, a sampling-based approach is taken for posterior inference that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) strategies.
2006 By J. Lundquist

Atmospheric releases of hazardous materials are highly effective means to impact large populations.

Author: J. K. Lundquist

Publisher:

ISBN: OCLC:316316327

Category:

Page: 10

View: 963

DOWNLOAD & READ
Atmospheric releases of hazardous materials are highly effective means to impact large populations. We propose an atmospheric event reconstruction framework that couples observed data and predictive computer-intensive dispersion models via Bayesian methodology. Due to the complexity of the model framework, a sampling-based approach is taken for posterior inference that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) strategies.
2005 By J. K. Lundquist

G. Johannesson et al., Sequential Monte-Carlo based framework for dynamic data-driven event reconstruction for atmospheric release, in Proceedings of the Joint Statistical Meeting, (American Statistical Association and Cosponsors, ...

Author: Stefka Fidanova

Publisher: Springer

ISBN: 9783319211336

Category: Technology & Engineering

Page: 244

View: 902

DOWNLOAD & READ
This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2014, held at Warsaw, Poland, September 7-10, 2014. The book presents recent advances in computational optimization. The volume includes important real problems like parameter settings for controlling processes in bioreactor and other processes, resource constrained project scheduling, infection distribution, molecule distance geometry, quantum computing, real-time management and optimal control, bin packing, medical image processing, localization the abrupt atmospheric contamination source and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.
2015-07-14 By Stefka Fidanova

This is a collaborative LDRD Exploratory Research project involving four directorates--Energy & Environment, Engineering, NAI and Computation.

Author:

Publisher:

ISBN: OCLC:68227957

Category:

Page:

View: 456

DOWNLOAD & READ
This is a collaborative LDRD Exploratory Research project involving four directorates--Energy & Environment, Engineering, NAI and Computation. The project seeks to answer the following critical questions regarding atmospheric releases--''How much material was released? When? Where? and What are the potential consequences?'' Inaccurate estimation of the source term can lead to gross errors, time delays during a crisis, and even fatalities. We are developing a capability that seamlessly integrates observational data streams with predictive models in order to provide the best possible estimates of unknown source term parameters, as well as optimal and timely situation analyses consistent with both models and data. Our approach utilizes Bayesian inference and stochastic sampling methods (Markov Chain and Sequential Monte Carlo) to reformulate the inverse problem into a solution based on efficient sampling of an ensemble of predictive simulations, guided by statistical comparisons with data. We are developing a flexible and adaptable data-driven event-reconstruction capability for atmospheric releases that provides (1) quantitative probabilistic estimates of the principal source-term parameters (e.g., the time-varying release rate and location); (2) predictions of increasing fidelity as an event progresses and additional data become available; and (3) analysis tools for sensor network design and uncertainty studies. Our computational framework incorporates multiple stochastic algorithms, operates with a range and variety of atmospheric models, and runs on multiple computer platforms, from workstations to large-scale computing resources. Our final goal is a multi-resolution capability for both real-time operational response and high fidelity multi-scale applications.
2005 By

The role of an event reconstruction capability in a case of an atmospheric release is to characterize the source by answering the critical questions--How much material was released?

Author: B. Kosovic

Publisher:

ISBN: OCLC:316326456

Category:

Page: 6

View: 520

DOWNLOAD & READ
The role of an event reconstruction capability in a case of an atmospheric release is to characterize the source by answering the critical questions--How much material was released? When? Where? and What are the potential consequences? Accurate estimation of the source term is essential to accurately predict plume dispersion, effectively manage the emergency response, and mitigate consequences in a case of an atmospheric release of hazardous material. We are developing a capability that seamlessly integrates observational data streams with predictive models in order to provide probabilistic estimates of unknown source term parameters consistent with both data and model predictions. Our approach utilizes Bayesian inference with stochastic sampling using Markov Chain and Sequential Monte Carlo methodology. The inverse dispersion problem is reformulated into a solution based on efficient sampling of an ensemble of predictive simulations, guided by statistical comparisons with data. We are developing a flexible and adaptable data-driven event-reconstruction capability for atmospheric releases that provides (1) quantitative probabilistic estimates of the principal source-term parameters (e.g., the time-varying release rate and location); (2) predictions of increasing fidelity as an event progresses and additional data become available; and (3) analysis tools for sensor network design and uncertainty studies. Our computational framework incorporates multiple stochastic algorithms, operates with a range and variety of atmospheric models, and runs on multiple computer platforms, from workstations to large-scale computing resources. Our final goal is a multi-resolution capability for both real-time operational response and high fidelity multi-scale applications.
2006 By B. Kosovic

In this context it is valuable to develop the emergency system which based on measurements of the concentration of ... 2.1 Synthetic Data Our main goal is to conduct dynamic inference on an unknown atmospheric release and its impact as ...

Author: Roman Wyrzykowski

Publisher: Springer

ISBN: 9783642551956

Category: Computers

Page: 775

View: 568

DOWNLOAD & READ
This two-volume-set (LNCS 8384 and 8385) constitutes the refereed proceedings of the 10th International Conference of Parallel Processing and Applied Mathematics, PPAM 2013, held in Warsaw, Poland, in September 2013. The 143 revised full papers presented in both volumes were carefully reviewed and selected from numerous submissions. The papers cover important fields of parallel/distributed/cloud computing and applied mathematics, such as numerical algorithms and parallel scientific computing; parallel non-numerical algorithms; tools and environments for parallel/distributed/cloud computing; applications of parallel computing; applied mathematics, evolutionary computing and metaheuristics.
2014-05-07 By Roman Wyrzykowski

... numerical determination of symmetric sparsity patterns ( DE94-016344 ) 05 P1096 N95-18354 Atmospheric Infrared Sounder ( NASA - CR - 197573 ] 05 21064 N95-18444 Radial basis function neural networks applied to NASA SSME data ( NASA ...

Author:

Publisher:

ISBN: UIUC:30112048646605

Category: Aeronautics

Page:

View: 468

DOWNLOAD & READ
1995 By

Author:

Publisher:

ISBN: UOM:39015040415815

Category: Mathematical statistics

Page:

View: 946

DOWNLOAD & READ
The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
1994 By

The following article, therefore, although based on some facts is based also on much conjecture and opinion, which I can only claim to be my own. In it I have indulged in what many will consider a great deal of wishful thinking, ...

Author:

Publisher:

ISBN:

Category:

Page: 116

View: 131

DOWNLOAD & READ
The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
1970-06 By

1997-14 ) 35 P3597 A97-43825 DATA RETRIEVAL The INTEGRAL Science Data Centre 82 p300 A97-11943 Retrieval of forest stem ... life reliability assessment 38 p1448 A97-23565 Aerospace applications of Weibull and Monte Carlo simulation with ...

Author:

Publisher:

ISBN: UOM:39015040314299

Category: Aeronautics

Page:

View: 140

DOWNLOAD & READ
1997 By