Metamodel-based nested sampling for model selection in eddy-current testing
| Autor: | Sandor Bilicz, Dominique Lesselier, Marc Lambert, Caifang Cai, Thomas Rodet |
|---|---|
| Přispěvatelé: | Laboratoire des signaux et systèmes (L2S), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Budapest University of Technology and Economics [Budapest] (BME), Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-École normale supérieure - Rennes (ENS Rennes)-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Génie électrique et électronique de Paris (GeePs), Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), ANR-13-MONU-0011,ByPASS,Méthodes Bayesiennes pour le diagnostic et la Probabilité de détection Assistée par la Simulation(2013), Université Paris-Seine-Université Paris-Seine-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
model selection MCMC Computer science 01 natural sciences Index Terms Nested Sampling symbols.namesake Surrogate model Numerical approximation [MATH.MATH-MP]Mathematics [math]/Mathematical Physics [math-ph] 0103 physical sciences Electrical and Electronic Engineering statistical inversion surrogate model 010303 astronomy & astrophysics Nested sampling algorithm 010302 applied physics metamodel Model selection Approximation algorithm non-destructive testing Markov chain Monte Carlo Statistical model marginal likelihood Marginal likelihood eddy-current Electronic Optical and Magnetic Materials Metamodeling [SPI.ELEC]Engineering Sciences [physics]/Electromagnetism symbols [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | IEEE Transactions on Magnetics IEEE Transactions on Magnetics, Institute of Electrical and Electronics Engineers, 2017, 53 (4), pp.6200912. ⟨10.1109/TMAG.2016.2635626⟩ IEEE Transactions on Magnetics, 2017, 53 (4), pp.6200912. ⟨10.1109/TMAG.2016.2635626⟩ |
| ISSN: | 0018-9464 |
| DOI: | 10.1109/TMAG.2016.2635626⟩ |
| Popis: | In non-destructive testing, model selection is a common problem, e.g., to determine the number of defects present in the inspected workpiece. Statistical model selection requires to approximate the marginal likelihood also called model evidence. Its numerical approximation is usually computationally expensive. Nested sampling (NS) offers a good compromise between estimation accuracy and computational cost. But, it requires to evaluate the forward model many times. Here, we first propose a general framework where data-fitting surrogate models are used to accelerate the computation. Then, improvements benefiting from surrogate modeling are introduced into the traditional NS algorithm to further reduce the computational cost. These improvements include the use of a sparse-grid surrogate model to deal with the “curse-of-dimensionality” in large dimensional problems and of the preestimated posterior space to save warming-up time. Based on eddy-current simulations, we show that this improved model selection approach has high model selection ability and can jointly perform model selection and parameter inversion. |
| Databáze: | OpenAIRE |
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