Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Michele Lungaroni"'
Publikováno v:
Complexity, Vol 2022 (2022)
It can be argued that the identification of sound mathematical models is the ultimate goal of any scientific endeavour. On the other hand, particularly in the investigation of complex systems and nonlinear phenomena, discriminating between alternativ
Externí odkaz:
https://doaj.org/article/abfa40fb800e47e88e9709101e145c98
Publikováno v:
Complexity, Vol 2019 (2019)
In the last years the reputation of medical, economic, and scientific expertise has been strongly damaged by a series of false predictions and contradictory studies. The lax application of statistical principles has certainly contributed to the uncer
Externí odkaz:
https://doaj.org/article/ffc8644b8a264af684aa6b7cb4c99830
Autor:
Andrea Murari, Emmanuele Peluso, Michele Lungaroni, Riccardo Rossi, Michela Gelfusa, JET Contributors
Publikováno v:
Applied Sciences, Vol 10, Iss 19, p 6683 (2020)
The inadequacies of basic physics models for disruption prediction have induced the community to increasingly rely on data mining tools. In the last decade, it has been shown how machine learning predictors can achieve a much better performance than
Externí odkaz:
https://doaj.org/article/e292b52356774145babaf4fce7186c70
Publikováno v:
Entropy, Vol 22, Iss 2, p 141 (2020)
The increasingly sophisticated investigations of complex systems require more robust estimates of the correlations between the measured quantities. The traditional Pearson correlation coefficient is easy to calculate but sensitive only to linear corr
Externí odkaz:
https://doaj.org/article/0bdf7c6aeee14da187001275346f38cf
Publikováno v:
Entropy, Vol 21, Iss 4, p 394 (2019)
The most widely used forms of model selection criteria, the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), are expressed in terms of synthetic indicators of the residual distribution: the variance and the mean-square
Externí odkaz:
https://doaj.org/article/5043cabb1da24d8e92019fb0afd427f2
Autor:
Andrea Murari, Michele Lungaroni, Emmanuele Peluso, Pasquale Gaudio, Ernesto Lerche, Luca Garzotti, Michela Gelfusa, JET Contributors
Publikováno v:
Entropy, Vol 20, Iss 9, p 627 (2018)
Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual inform
Externí odkaz:
https://doaj.org/article/0043bc74062d483ca46eb8153943b59d
Autor:
Andrea Murari, Riccardo Rossi, Luca Spolladore, Michele Lungaroni, Pasquale Gaudio, Michela Gelfusa
In many fields of science, various types of models are available to describe phenomena, observations and the results of experiments. In the last decades, given the enormous advances of information gathering technologies, also machine learning techniq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::797974fd0676ed0b61bdf2781b058475
https://doi.org/10.21203/rs.3.rs-2449577/v1
https://doi.org/10.21203/rs.3.rs-2449577/v1
Autor:
Stefan Matejcik, Francesco Romanelli, Augusto Pereira González, Jesús Vega, Bohdan Bieg, Emmanuele Peluso, Vladislav Plyusnin, José Vicente, Alberto Loarte, Michele Lungaroni, Bor Kos, Andrea Murari, Axel Jardin, Rajnikant Makwana, CHIARA MARCHETTO, Choong-Seock Chang, Manuel Garcia-munoz
Publikováno v:
Fusion engineering and design 130 (2018): 62–68. doi:10.1016/j.fusengdes.2018.02.087
info:cnr-pdr/source/autori:Lungaroni M.; Murari A.; Peluso E.; Vega J.; Farias G.; Gelfusa M./titolo:On the potential of ruled-based machine learning for disruption prediction on JET/doi:10.1016%2Fj.fusengdes.2018.02.087/rivista:Fusion engineering and design/anno:2018/pagina_da:62/pagina_a:68/intervallo_pagine:62–68/volume:130
Fusion Engineering and Design
info:cnr-pdr/source/autori:Lungaroni M.; Murari A.; Peluso E.; Vega J.; Farias G.; Gelfusa M./titolo:On the potential of ruled-based machine learning for disruption prediction on JET/doi:10.1016%2Fj.fusengdes.2018.02.087/rivista:Fusion engineering and design/anno:2018/pagina_da:62/pagina_a:68/intervallo_pagine:62–68/volume:130
Fusion Engineering and Design
In the last years, it has become apparent that detecting disruptions with sufficient anticipation time is an essential but not exclusive task of predictors. It is also important that the prediction is accompanied by appropriate qualifications of its
Autor:
Alexander Lukin, Pasqualino Gaudio, Stefan Matejcik, Soare Sorin, Francesco Romanelli, Bohdan Bieg, Luca Garzotti, Emmanuele Peluso, Michela Gelfusa, Vladislav Plyusnin, José Vicente, Alberto Loarte, Michele Lungaroni, Bor Kos, Andrea Murari, Axel Jardin, Rajnikant Makwana, CHIARA MARCHETTO, William Tang, Choong-Seock Chang, Manuel Garcia-munoz
Publikováno v:
Entropy (Basel, Online) 20 (2018): 1–14. doi:10.3390/e20090627
info:cnr-pdr/source/autori:Murari A.; Lungaroni M.; Peluso E.; Gaudio P.; Lerche E.; Garzotti L.; Gelfusa M./titolo:On the use of transfer entropy to investigate the time horizon of causal influences between signals/doi:10.3390%2Fe20090627/rivista:Entropy (Basel, Online)/anno:2018/pagina_da:1/pagina_a:14/intervallo_pagine:1–14/volume:20
Entropy, Vol 20, Iss 9, p 627 (2018)
Entropy
idUS. Depósito de Investigación de la Universidad de Sevilla
instname
Volume 20
Issue 9
idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
info:cnr-pdr/source/autori:Murari A.; Lungaroni M.; Peluso E.; Gaudio P.; Lerche E.; Garzotti L.; Gelfusa M./titolo:On the use of transfer entropy to investigate the time horizon of causal influences between signals/doi:10.3390%2Fe20090627/rivista:Entropy (Basel, Online)/anno:2018/pagina_da:1/pagina_a:14/intervallo_pagine:1–14/volume:20
Entropy, Vol 20, Iss 9, p 627 (2018)
Entropy
idUS. Depósito de Investigación de la Universidad de Sevilla
instname
Volume 20
Issue 9
idUS: Depósito de Investigación de la Universidad de Sevilla
Universidad de Sevilla (US)
Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual inform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5cffbed667073ded0059757b3bb31188
http://hdl.handle.net/10138/306771
http://hdl.handle.net/10138/306771
Autor:
Alexander Lukin, Pasqualino Gaudio, Stefan Matejcik, Soare Sorin, Francesco Romanelli, Emilio Blanco, Bohdan Bieg, Emmanuele Peluso, Vladislav Plyusnin, José Vicente, Alberto Loarte, Michele Lungaroni, Andrea Murari, Rajnikant Makwana, CHIARA MARCHETTO, Marco Wischmeier, Choong-Seock Chang, Aneta Gójska, Manuel Garcia-munoz
Publikováno v:
Nuclear Fusion
Nuclear fusion 57 (2017). doi:10.1088/0029-5515/57/1/016024
info:cnr-pdr/source/autori:Murari A.; Peluso E.; Vega J.; Gelfusa M.; Lungaroni M.; Gaudio P.; Martinez F. J./titolo:Determining the prediction limits of models and classifiers with applications for disruption prediction in JET/doi:10.1088%2F0029-5515%2F57%2F1%2F016024/rivista:Nuclear fusion/anno:2017/pagina_da:/pagina_a:/intervallo_pagine:/volume:57
Nuclear fusion 57 (2017). doi:10.1088/0029-5515/57/1/016024
info:cnr-pdr/source/autori:Murari A.; Peluso E.; Vega J.; Gelfusa M.; Lungaroni M.; Gaudio P.; Martinez F. J./titolo:Determining the prediction limits of models and classifiers with applications for disruption prediction in JET/doi:10.1088%2F0029-5515%2F57%2F1%2F016024/rivista:Nuclear fusion/anno:2017/pagina_da:/pagina_a:/intervallo_pagine:/volume:57
Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the ca