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Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Tillämpad beräkningsvetenskap.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Tillämpad beräkningsvetenskap.ORCID-id: 0000-0002-3614-1732
2015 (engelsk)Inngår i: Numerical Mathematics and Advanced Applications: ENUMATH 2013, Springer, 2015, s. 519-527Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
Springer, 2015. s. 519-527
Serie
Lecture Notes in Computational Science and Engineering ; 103
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-237184DOI: 10.1007/978-3-319-10705-9_51ISBN: 978-3-319-10704-2 (tryckt)OAI: oai:DiVA.org:uu-237184DiVA, id: diva2:767002
Konferanse
ENUMATH 2013
Prosjekter
eSSENCEUPMARCTilgjengelig fra: 2014-10-31 Laget: 2014-11-28 Sist oppdatert: 2018-11-12bibliografisk kontrollert
Inngår i avhandling
1. Parallelism and efficiency in discrete-event simulation
Åpne denne publikasjonen i ny fane eller vindu >>Parallelism and efficiency in discrete-event simulation
2015 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Discrete-event models depict systems where a discrete state is repeatedly altered by instantaneous changes in time, the events of the model. Such models have gained popularity in fields such as Computational Systems Biology or Computational Epidemiology due to the high modeling flexibility and the possibility to easily combine stochastic and deterministic dynamics. However, the system size of modern discrete-event models is growing and/or they need to be simulated at long time periods. Thus, efficient simulation algorithms are required, as well as the possibility to harness the compute potential of modern multicore computers. Due to the sequential design of simulators, parallelization of discrete event simulations is not trivial. This thesis discusses event-based modeling and sensitivity analysis and also examines ways to increase the efficiency of discrete-event simulations and to scale models involving deterministic and stochastic spatial dynamics on a large number of processor cores.

sted, utgiver, år, opplag, sider
Uppsala University, 2015
Serie
IT licentiate theses / Uppsala University, Department of Information Technology, ISSN 1404-5117 ; 2015-004
HSV kategori
Forskningsprogram
Beräkningsvetenskap
Identifikatorer
urn:nbn:se:uu:diva-264756 (URN)
Veileder
Prosjekter
UPMARCeSSENCE
Tilgjengelig fra: 2015-10-14 Laget: 2015-10-16 Sist oppdatert: 2018-11-12bibliografisk kontrollert
2. Parallelism in Event-Based Computations with Applications in Biology
Åpne denne publikasjonen i ny fane eller vindu >>Parallelism in Event-Based Computations with Applications in Biology
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Event-based models find frequent usage in fields such as computational physics and biology as they may contain both continuous and discrete state variables and may incorporate both deterministic and stochastic state transitions. If the state transitions are stochastic, computer-generated random numbers are used to obtain the model solution. This type of event-based computations is also known as Monte-Carlo simulation.

In this thesis, I study different approaches to execute event-based computations on parallel computers. This ultimately allows users to retrieve their simulation results in a fraction of the original computation time. As system sizes grow continuously or models have to be simulated at longer time scales, this is a necessary approach for current computational tasks.

More specifically, I propose several ways to asynchronously simulate such models on parallel shared-memory computers, for example using parallel discrete-event simulation or task-based computing. The particular event-based models studied herein find applications in systems biology, computational epidemiology and computational neuroscience.

In the presented studies, the proposed methods allow for high efficiency of the parallel simulation, typically scaling well with the number of used computer cores. As the scaling typically depends on individual model properties, the studies also investigate which quantities have the greatest impact on the simulation performance.

Finally, the presented studies include other insights into event-based computations, such as methods how to estimate parameter sensitivity in stochastic models and how to simulate models that include both deterministic and stochastic state transitions.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2017. s. 48
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1586
Emneord
Event-based computations, Parallel algorithms, Discrete-event simulation, Monte-Carlo methods, Systems biology.
HSV kategori
Forskningsprogram
Beräkningsvetenskap
Identifikatorer
urn:nbn:se:uu:diva-332009 (URN)978-91-513-0125-9 (ISBN)
Disputas
2017-12-11, 2347, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (engelsk)
Opponent
Veileder
Prosjekter
UPMARC
Tilgjengelig fra: 2017-11-30 Laget: 2017-10-22 Sist oppdatert: 2018-03-07

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