Pub Date : 1900-01-01DOI: 10.7712/120219.6337.18843
N. Le, V. Mallet, I. Korsakissok, A. Mathieu, R. Périllat
{"title":"CALIBRATION OF A SURROGATE DISPERSION MODEL APPLIED TO THE FUKUSHIMA NUCLEAR DISASTER","authors":"N. Le, V. Mallet, I. Korsakissok, A. Mathieu, R. Périllat","doi":"10.7712/120219.6337.18843","DOIUrl":"https://doi.org/10.7712/120219.6337.18843","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121862026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6344.18653
C. Pepi, M. Gioffrè, M. Grigoriu, H. Matthies
{"title":"BAYESIAN UPDATING OF CABLE STAYED FOOTBRIDGE MODEL PARAMETERS USING DYNAMIC MEASUREMENTS","authors":"C. Pepi, M. Gioffrè, M. Grigoriu, H. Matthies","doi":"10.7712/120219.6344.18653","DOIUrl":"https://doi.org/10.7712/120219.6344.18653","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125472447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6354.18702
N. Gray, M. Angelis, S. Ferson
. Although engineers often recognise the advantages of applying uncertainty analysis to their complex simulations, they often lack the time, patience or expertise to undertake that analysis. We describe a software tool, named puffin, that takes existing code and converts in to uncertainty aware code in the same language making use of intrusive uncertainty propagation techniques. It can work either automatically or with user specification of the uncertainties involved in the system.
{"title":"COMPUTING WITH UNCERTAINTY: INTRODUCING PUFFIN THE AUTOMATIC UNCERTAINTY COMPILER","authors":"N. Gray, M. Angelis, S. Ferson","doi":"10.7712/120219.6354.18702","DOIUrl":"https://doi.org/10.7712/120219.6354.18702","url":null,"abstract":". Although engineers often recognise the advantages of applying uncertainty analysis to their complex simulations, they often lack the time, patience or expertise to undertake that analysis. We describe a software tool, named puffin, that takes existing code and converts in to uncertainty aware code in the same language making use of intrusive uncertainty propagation techniques. It can work either automatically or with user specification of the uncertainties involved in the system.","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115195661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6332.18536
Conradus Van Mierlo, M. Faes, D. Moens
{"title":"IDENTIFICATION OF VISCO-PLASTIC MATERIAL MODEL PARAMETERS USING INTERVAL FIELDS","authors":"Conradus Van Mierlo, M. Faes, D. Moens","doi":"10.7712/120219.6332.18536","DOIUrl":"https://doi.org/10.7712/120219.6332.18536","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"167 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6347.18786
Thomas Oberleiter, A. Müller, T. Hausotte, K. Willner
Virtual approaches to manufacturing processes are a common tool in developing components today. Simulations are always containing uncertainties like simplifying assumptions in computer aided modelling, material deviations, fluctuating external loads or other known and unknown influences. To integrate such uncertainties in an early design stage, the input parameters should be defined as intervals, because insufficient data may be available at this stage to provide probability distributions. To consider such epistemic uncertainties, a large number of intervals can be merged into a fuzzy number. For each interval a membership value is assigned which depends on the interval limits and an expert estimation. However, this interval modelling leads to a very high number of expensive evaluations, which is not feasible for a high number of uncertain input parameters. To reduce the calculation time, surrogate models are used. Here, the full model is evaluated only at some grid points and the system response is approximated by mathematical approaches. Design and Analysis of Computer Experiments (DACE) offers a suitable surrogate model based on the Kriging method. The system model substituted in this way can be evaluated in an efficient way, but in addition to the uncertain simulation results, the approximation error dependent on the surrogate model has to be considered. Investigations of first prototypes lead to new knowledge that can be used to improve the surrogate model. Measurements, however, also include errors that are composed of systematic and random errors. The systematic measurement errors are specific errors for each measuring system and task, which are usually corrected during the measurement. However, an estimation of the random measurement error, which represents the precision of the measurement can be taken into account. Two methods are presented. Either an additional constant term is implemented in the standard Kriging or a superposition of two standard Kriging models, which are based on the simulation data and the measurement data, is used. As an application example a cold forging process of a steel gearwheel is employed.
{"title":"SURROGATE MODELING CONSIDERING MEASURING DATA AND THEIR MEASUREMENT UNCERTAINTY","authors":"Thomas Oberleiter, A. Müller, T. Hausotte, K. Willner","doi":"10.7712/120219.6347.18786","DOIUrl":"https://doi.org/10.7712/120219.6347.18786","url":null,"abstract":"Virtual approaches to manufacturing processes are a common tool in developing components today. Simulations are always containing uncertainties like simplifying assumptions in computer aided modelling, material deviations, fluctuating external loads or other known and unknown influences. To integrate such uncertainties in an early design stage, the input parameters should be defined as intervals, because insufficient data may be available at this stage to provide probability distributions. To consider such epistemic uncertainties, a large number of intervals can be merged into a fuzzy number. For each interval a membership value is assigned which depends on the interval limits and an expert estimation. However, this interval modelling leads to a very high number of expensive evaluations, which is not feasible for a high number of uncertain input parameters. To reduce the calculation time, surrogate models are used. Here, the full model is evaluated only at some grid points and the system response is approximated by mathematical approaches. Design and Analysis of Computer Experiments (DACE) offers a suitable surrogate model based on the Kriging method. The system model substituted in this way can be evaluated in an efficient way, but in addition to the uncertain simulation results, the approximation error dependent on the surrogate model has to be considered. Investigations of first prototypes lead to new knowledge that can be used to improve the surrogate model. Measurements, however, also include errors that are composed of systematic and random errors. The systematic measurement errors are specific errors for each measuring system and task, which are usually corrected during the measurement. However, an estimation of the random measurement error, which represents the precision of the measurement can be taken into account. Two methods are presented. Either an additional constant term is implemented in the standard Kriging or a superposition of two standard Kriging models, which are based on the simulation data and the measurement data, is used. As an application example a cold forging process of a steel gearwheel is employed.","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122065287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6356.18823
E. Auer, W. Luther
{"title":"RECOMMENDER TECHNIQUES FOR SOFTWARE WITH RESULT VERIFICATION","authors":"E. Auer, W. Luther","doi":"10.7712/120219.6356.18823","DOIUrl":"https://doi.org/10.7712/120219.6356.18823","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114249896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6369.18408
D. Valis, K. Hasilová
{"title":"PRINCIPLES FOR UNCERTAINTY ASSESSMENT IN KERNEL SMOOTHING ESTIMATIONS","authors":"D. Valis, K. Hasilová","doi":"10.7712/120219.6369.18408","DOIUrl":"https://doi.org/10.7712/120219.6369.18408","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6352.18797
G. Geraci, L. Swiler, J. Crussell, B. Debusschere
. Communication networks have evolved to a level of sophistication that requires computer models and numerical simulations to understand and predict their behavior. A network simulator is a software that enables the network designer to model several components of a computer network such as nodes, routers, switches and links and events such as data transmissions and packet errors in order to obtain device and network level metrics. Network simulations, as many other numerical approximations that model complex systems, are subject to the specification of parameters and operative conditions of the system. Very often the full characterization of the system and their input is not possible, therefore Uncertainty Quantification (UQ) strategies need to be deployed to evaluate the statistics of its response and behavior. UQ techniques, despite the advancements in the last two decades, still suffer in the presence of a large number of uncertain variables and when the regularity of the systems response cannot be guaranteed. In this context, multifidelity approaches have gained popularity in the UQ community recently due to their flexibility and robustness with respect to these challenges. The main idea behind these techniques is to extract information from a limited number of high-fidelity model realizations and complement them with a much larger number of a set of lower fidelity evaluations. The final result is an estimator with a much lower variance, i.e. a more accurate and reliable estimator can be obtained. In this contribution we investigate the possibility to deploy multifidelity UQ strategies to computer network analysis. Two numerical configurations are studied based on a simplified network with one client and one server. Preliminary results for these tests suggest that multifidelity sampling techniques might be used as effective tools for UQ tools in network applications
{"title":"EXPLORATION OF MULTIFIDELITY APPROACHES FOR UNCERTAINTY QUANTIFICATION IN NETWORK APPLICATIONS","authors":"G. Geraci, L. Swiler, J. Crussell, B. Debusschere","doi":"10.7712/120219.6352.18797","DOIUrl":"https://doi.org/10.7712/120219.6352.18797","url":null,"abstract":". Communication networks have evolved to a level of sophistication that requires computer models and numerical simulations to understand and predict their behavior. A network simulator is a software that enables the network designer to model several components of a computer network such as nodes, routers, switches and links and events such as data transmissions and packet errors in order to obtain device and network level metrics. Network simulations, as many other numerical approximations that model complex systems, are subject to the specification of parameters and operative conditions of the system. Very often the full characterization of the system and their input is not possible, therefore Uncertainty Quantification (UQ) strategies need to be deployed to evaluate the statistics of its response and behavior. UQ techniques, despite the advancements in the last two decades, still suffer in the presence of a large number of uncertain variables and when the regularity of the systems response cannot be guaranteed. In this context, multifidelity approaches have gained popularity in the UQ community recently due to their flexibility and robustness with respect to these challenges. The main idea behind these techniques is to extract information from a limited number of high-fidelity model realizations and complement them with a much larger number of a set of lower fidelity evaluations. The final result is an estimator with a much lower variance, i.e. a more accurate and reliable estimator can be obtained. In this contribution we investigate the possibility to deploy multifidelity UQ strategies to computer network analysis. Two numerical configurations are studied based on a simplified network with one client and one server. Preliminary results for these tests suggest that multifidelity sampling techniques might be used as effective tools for UQ tools in network applications","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134204245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6326.18759
T. Alderucci, F. Genovese, G. Muscolino
{"title":"RESPONSE SENSITIVITY OF STRUCTURAL SYSTEMS SUBJECTED TO FULLY NON-STATIONARY RANDOM PROCESSES","authors":"T. Alderucci, F. Genovese, G. Muscolino","doi":"10.7712/120219.6326.18759","DOIUrl":"https://doi.org/10.7712/120219.6326.18759","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129577800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.7712/120219.6366.18426
Chen Song, V. Heuveline
{"title":"UNCERTAINTY ASSESSMENT OF THE BLOOD DAMAGE IN A FDA BLOOD PUMP","authors":"Chen Song, V. Heuveline","doi":"10.7712/120219.6366.18426","DOIUrl":"https://doi.org/10.7712/120219.6366.18426","url":null,"abstract":"","PeriodicalId":153829,"journal":{"name":"Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130830067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：