Optimal design techniques for distributed parameter systems
A wide number of inverse problems consist in selecting best parameter values of a given mathematical model based fits to measured data. These are usually formulated as optimization problems and the accuracy of their solutions depends not only on the chosen optimization scheme but also on the given d...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97815108_v_n_p83_Banks |
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todo:paper_97815108_v_n_p83_Banks2023-10-03T16:43:48Z Optimal design techniques for distributed parameter systems Banks, H.T. Rubio, D. Saintier, N. Troparevsky, M.I. Kang W. Zhang Q. Fahroo F. Estimation Optimal systems Optimization Distributed parameter systems Optimal locations Optimal observation Optimization problems Optimization scheme Ordinary least squares Sensitivity functions Source identification Inverse problems A wide number of inverse problems consist in selecting best parameter values of a given mathematical model based fits to measured data. These are usually formulated as optimization problems and the accuracy of their solutions depends not only on the chosen optimization scheme but also on the given data. The problem of collecting data in the "best way" in order to assure a statistically efficient estimate of the parameter is known as Optimal Design. In this work we consider the problem of finding optimal locations for source identification in the 3D unit sphere from data on its boundary. We apply three different optimal design criteria to this 3D problem: the Incremental Generalized Sensitivity Function (IGSF), the classical D-optimal criterion and the SE-criterion recently introduced in [3]. The estimation of the parameters is then obtained by means of the Ordinary Least Square procedure on the resulting optimal observation points and compared to that for a uniform observation mesh. In order to analyze the performance of each strategy, the data are numerically simulated and the estimated values are compared with the values used for simulation. © Copyright SIAM. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97815108_v_n_p83_Banks |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
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R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Estimation Optimal systems Optimization Distributed parameter systems Optimal locations Optimal observation Optimization problems Optimization scheme Ordinary least squares Sensitivity functions Source identification Inverse problems |
| spellingShingle |
Estimation Optimal systems Optimization Distributed parameter systems Optimal locations Optimal observation Optimization problems Optimization scheme Ordinary least squares Sensitivity functions Source identification Inverse problems Banks, H.T. Rubio, D. Saintier, N. Troparevsky, M.I. Kang W. Zhang Q. Fahroo F. Optimal design techniques for distributed parameter systems |
| topic_facet |
Estimation Optimal systems Optimization Distributed parameter systems Optimal locations Optimal observation Optimization problems Optimization scheme Ordinary least squares Sensitivity functions Source identification Inverse problems |
| description |
A wide number of inverse problems consist in selecting best parameter values of a given mathematical model based fits to measured data. These are usually formulated as optimization problems and the accuracy of their solutions depends not only on the chosen optimization scheme but also on the given data. The problem of collecting data in the "best way" in order to assure a statistically efficient estimate of the parameter is known as Optimal Design. In this work we consider the problem of finding optimal locations for source identification in the 3D unit sphere from data on its boundary. We apply three different optimal design criteria to this 3D problem: the Incremental Generalized Sensitivity Function (IGSF), the classical D-optimal criterion and the SE-criterion recently introduced in [3]. The estimation of the parameters is then obtained by means of the Ordinary Least Square procedure on the resulting optimal observation points and compared to that for a uniform observation mesh. In order to analyze the performance of each strategy, the data are numerically simulated and the estimated values are compared with the values used for simulation. © Copyright SIAM. |
| format |
CONF |
| author |
Banks, H.T. Rubio, D. Saintier, N. Troparevsky, M.I. Kang W. Zhang Q. Fahroo F. |
| author_facet |
Banks, H.T. Rubio, D. Saintier, N. Troparevsky, M.I. Kang W. Zhang Q. Fahroo F. |
| author_sort |
Banks, H.T. |
| title |
Optimal design techniques for distributed parameter systems |
| title_short |
Optimal design techniques for distributed parameter systems |
| title_full |
Optimal design techniques for distributed parameter systems |
| title_fullStr |
Optimal design techniques for distributed parameter systems |
| title_full_unstemmed |
Optimal design techniques for distributed parameter systems |
| title_sort |
optimal design techniques for distributed parameter systems |
| url |
http://hdl.handle.net/20.500.12110/paper_97815108_v_n_p83_Banks |
| work_keys_str_mv |
AT banksht optimaldesigntechniquesfordistributedparametersystems AT rubiod optimaldesigntechniquesfordistributedparametersystems AT saintiern optimaldesigntechniquesfordistributedparametersystems AT troparevskymi optimaldesigntechniquesfordistributedparametersystems AT kangw optimaldesigntechniquesfordistributedparametersystems AT zhangq optimaldesigntechniquesfordistributedparametersystems AT fahroof optimaldesigntechniquesfordistributedparametersystems |
| _version_ |
1807324144033857536 |