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ISSN Druckformat: 2152-5080
ISSN Online: 2152-5099
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UNCERTAINTY QUANTIFICATION IN COMPUTATIONAL PREDICTIVE MODELS FOR FLUID DYNAMICS USING A WORKFLOW MANAGEMENT ENGINE
ABSTRAKT
Computational simulation of complex engineered systems requires intensive computation and a significant amount of data management. Today, this management is often carried out on a case-by-case basis and requires great effort to track it. This is due to the complexity of controlling a large amount of data flowing along a chain of simulations. Moreover, many times there is a need to explore parameter variability for the same set of data. On a case-by-case basis, there is no register of data involved in the simulation, making this process prone to errors. In addition, if the user wants to analyze the behavior of a simulation sample, then he/she must wait until the end of the whole simulation. In this context, techniques and methodologies of scientific workflows can improve the management of simulations. Parameter variability can be put in the general context of uncertainty quantification (UQ), which provides a rational perspective for analysts and decision makers. The objective of this work is to use scientific workflows to provide a systematic approach in: (i) modeling UQ numerical experiments as scientific workflows, (ii) offering query tools to evaluate UQ processes at runtime, (iii) managing the UQ analysis, and (iv) managing UQ in parallel executions. When using scientific workflow engines, one can collect data in a transparent manner, allowing execution steering, the postassessment of results, and providing the information for reexecuting the experiment, thereby ensuring reproducibility, an essential characteristic in a scientific or engineering computational experiment.
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Dias Jonas, Guerra Gabriel, Rochinha Fernando, Coutinho Alvaro L.G.A., Valduriez Patrick, Mattoso Marta, Data-centric iteration in dynamic workflows, Future Generation Computer Systems, 46, 2015. Crossref
-
Mattoso Marta, Dias Jonas, Ocaña Kary A.C.S., Ogasawara Eduardo, Costa Flavio, Horta Felipe, Silva Vítor, de Oliveira Daniel, Dynamic steering of HPC scientific workflows: A survey, Future Generation Computer Systems, 46, 2015. Crossref
-
Dias Jonas, Ogasawara Eduardo, de Oliveira Daniel, Porto Fabio, Valduriez Patrick, Mattoso Marta, Algebraic dataflows for big data analysis, 2013 IEEE International Conference on Big Data, 2013. Crossref
-
Guerra Gabriel M., Zio Souleymane, Camata Jose J., Dias Jonas, Elias Renato N., Mattoso Marta, B. Paraizo Paulo L., G. A. Coutinho Alvaro L., Rochinha Fernando A., Uncertainty quantification in numerical simulation of particle-laden flows, Computational Geosciences, 20, 1, 2016. Crossref
-
Marinho Anderson, de Oliveira Daniel, Ogasawara Eduardo, Silva Vitor, Ocaña Kary, Murta Leonardo, Braganholo Vanessa, Mattoso Marta, Deriving scientific workflows from algebraic experiment lines: A practical approach, Future Generation Computer Systems, 68, 2017. Crossref
-
Zio Souleymane, da Costa Henrique F., Guerra Gabriel M., Paraizo Paulo L.B., Camata Jose J., Elias Renato N., Coutinho Alvaro L.G.A., Rochinha Fernando A., Bayesian assessment of uncertainty in viscosity closure models for turbidity currents computations, Computer Methods in Applied Mechanics and Engineering, 342, 2018. Crossref
-
Pintas Julliano Trindade, de Oliveira Daniel, Ocaña Kary A. C. S., Ogasawara Eduardo, Mattoso Marta, SciLightning: A Cloud Provenance-Based Event Notification for Parallel Workflows, in Service-Oriented Computing – ICSOC 2013 Workshops, 8377, 2014. Crossref
-
Oliveira Douglas, Porto Fábio, Boeres Cristina, Oliveira Daniel, Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning, Concurrency and Computation: Practice and Experience, 33, 5, 2021. Crossref
-
Souza Renan, Azevedo Leonardo G., Lourenço Vítor, Soares Elton, Thiago Raphael, Brandão Rafael, Civitarese Daniel, Vital Brazil Emilio, Moreno Marcio, Valduriez Patrick, Mattoso Marta, Cerqueira Renato, Netto Marco A. S., Workflow provenance in the lifecycle of scientific machine learning, Concurrency and Computation: Practice and Experience, 34, 14, 2022. Crossref
-
Ferreira da Silva Rafael, Filgueira Rosa, Pietri Ilia, Jiang Ming, Sakellariou Rizos, Deelman Ewa, A characterization of workflow management systems for extreme-scale applications, Future Generation Computer Systems, 75, 2017. Crossref
-
de A.R. Gonçalves João Carlos, de Oliveira Daniel, Ocaña Kary A. C. S., Ogasawara Eduardo, Mattoso Marta, Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries, in Provenance and Annotation of Data and Processes, 7525, 2012. Crossref