dc.creator | Papadimitriou C., Argyris C., Chatzi E. | en |
dc.date.accessioned | 2023-01-31T09:42:14Z | |
dc.date.available | 2023-01-31T09:42:14Z | |
dc.date.issued | 2017 | |
dc.identifier.isbn | 9781138028470 | |
dc.identifier.uri | http://hdl.handle.net/11615/77569 | |
dc.description.abstract | An information theoretic framework for optimal experimental design is presented. The objective function is rooted in information theory, and is the expected Kullback-Leibler divergence between the prior and posterior pdf in a Bayesian framework. In this way we seek designs which will yield data that are most informative for model parameter inference. In general, the objective function has to be estimated by a Monte Carlo sum, which means that its evaluation requires a large number of model runs. Asymptotic approximations are introduced to significantly reduce these runs. The optimization of the objective function is performed using stochastic optimization methods such as CMA-ES to avoid premature convergence to local optimal usually manifested in optimal experimental design problems. The framework is demonstrated using applications from mechanics. Two optimal sensor placement problems are solved: 1) parameter estimation in non-linear model of simply supported beam under uncertain load, 2) modal identification. © 2017 Taylor & Francis Group, London. | en |
dc.language.iso | en | en |
dc.source | Life-Cycle of Engineering Systems: Emphasis on Sustainable Civil Infrastructure - 5th International Symposium on Life-Cycle Engineering, IALCCE 2016 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018628516&partnerID=40&md5=3cc7cc71a90f99a7a9ee2552b492fb11 | |
dc.subject | Information theory | en |
dc.subject | Network function virtualization | en |
dc.subject | Optimization | en |
dc.subject | Statistics | en |
dc.subject | Uncertainty analysis | en |
dc.subject | Asymptotic approximation | en |
dc.subject | Kullback Leibler divergence | en |
dc.subject | Modal identification | en |
dc.subject | Optimal experimental designs | en |
dc.subject | Optimal sensor placement problem | en |
dc.subject | Pre-mature convergences | en |
dc.subject | Simply supported beams | en |
dc.subject | Stochastic optimization methods | en |
dc.subject | Life cycle | en |
dc.subject | CRC Press/Balkema | en |
dc.title | An information theoretic framework for optimal experimental design | en |
dc.type | conferenceItem | en |