Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Eccomas Proceedia UNCECOMP (2023) |
| Herausgeber/-innen | M. Papadrakakis, V. Papadopoulos, G. Stefanou |
| Erscheinungsort | Athens |
| Seiten | 419-436 |
| Seitenumfang | 18 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2023 - Athens, Griechenland Dauer: 12 Juni 2023 → 14 Juni 2023 |
Publikationsreihe
| Name | UNCECOMP Proceedings |
|---|---|
| ISSN (Print) | 2623-3339 |
Abstract
This work presents a new framework for uncertainty quantification developed as a package in the Julia programming language called UncertaintyQuantification.jl. Julia is a modern high-level dynamic programming language ideally suited for tasks like data analysis and scientific computing. UncertaintyQuantification.jl was developed from the ground up to be generalized and flexible while at the same time being easy to use. Leveraging the features of a modern language such as Julia allows to write efficient, fast and easy to read code. Especially noteworthy is Julia’s core feature multiple dispatch which enables us to, for example, develop methods with a large number of varying simulation schemes such as standard Monte Carlo, Sobol sampling, Halton sampling, etc., yet minimal code duplication. Current features of UncertaintyQuantification.jl include simulation based reliability analysis using a large array of sampling schemes, local and global sensititivity analysis, meta modelling techniques such as response surface methodology or polynomial chaos expansion as well as the connection to external solvers by injecting values into plain text files as inputs. Through Julia’s existing distributed computing capabilities all available methods can be easily run on existing clusters with just a few lines of extra code.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Theoretische Informatik und Mathematik
- Informatik (insg.)
- Angewandte Informatik
- Mathematik (insg.)
- Modellierung und Simulation
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Mathematik (insg.)
- Steuerung und Optimierung
- Mathematik (insg.)
- Diskrete Mathematik und Kombinatorik
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- RIS
Eccomas Proceedia UNCECOMP (2023). Hrsg. / M. Papadrakakis; V. Papadopoulos; G. Stefanou. Athens, 2023. S. 419-436 (UNCECOMP Proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
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T2 - 5th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2023
AU - Behrensdorf, Jasper
AU - Gray, Ander
AU - Broggi, Matteo
AU - Beer, Michael
N1 - .
PY - 2023
Y1 - 2023
N2 - This work presents a new framework for uncertainty quantification developed as a package in the Julia programming language called UncertaintyQuantification.jl. Julia is a modern high-level dynamic programming language ideally suited for tasks like data analysis and scientific computing. UncertaintyQuantification.jl was developed from the ground up to be generalized and flexible while at the same time being easy to use. Leveraging the features of a modern language such as Julia allows to write efficient, fast and easy to read code. Especially noteworthy is Julia’s core feature multiple dispatch which enables us to, for example, develop methods with a large number of varying simulation schemes such as standard Monte Carlo, Sobol sampling, Halton sampling, etc., yet minimal code duplication. Current features of UncertaintyQuantification.jl include simulation based reliability analysis using a large array of sampling schemes, local and global sensititivity analysis, meta modelling techniques such as response surface methodology or polynomial chaos expansion as well as the connection to external solvers by injecting values into plain text files as inputs. Through Julia’s existing distributed computing capabilities all available methods can be easily run on existing clusters with just a few lines of extra code.
AB - This work presents a new framework for uncertainty quantification developed as a package in the Julia programming language called UncertaintyQuantification.jl. Julia is a modern high-level dynamic programming language ideally suited for tasks like data analysis and scientific computing. UncertaintyQuantification.jl was developed from the ground up to be generalized and flexible while at the same time being easy to use. Leveraging the features of a modern language such as Julia allows to write efficient, fast and easy to read code. Especially noteworthy is Julia’s core feature multiple dispatch which enables us to, for example, develop methods with a large number of varying simulation schemes such as standard Monte Carlo, Sobol sampling, Halton sampling, etc., yet minimal code duplication. Current features of UncertaintyQuantification.jl include simulation based reliability analysis using a large array of sampling schemes, local and global sensititivity analysis, meta modelling techniques such as response surface methodology or polynomial chaos expansion as well as the connection to external solvers by injecting values into plain text files as inputs. Through Julia’s existing distributed computing capabilities all available methods can be easily run on existing clusters with just a few lines of extra code.
KW - Julia
KW - Simulation
KW - Software
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=85175789929&partnerID=8YFLogxK
U2 - 10.7712/120223.10347.19810
DO - 10.7712/120223.10347.19810
M3 - Conference contribution
T3 - UNCECOMP Proceedings
SP - 419
EP - 436
BT - Eccomas Proceedia UNCECOMP (2023)
A2 - Papadrakakis, M.
A2 - Papadopoulos, V.
A2 - Stefanou, G.
CY - Athens
Y2 - 12 June 2023 through 14 June 2023
ER -