A Model Falsification Approach to Learning in Non-Stationary Environments for Experimental Design

The application of data driven machine learning and advanced statistical tools to complex physics experiments, such as Magnetic Confinement Nuclear Fusion, can be problematic, due the varying conditions of the systems to be studied. In particular, new experiments have to be planned in unexplored reg...

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Detalles Bibliográficos
Autores principales: Murari, Andrea, Lungaroni, Michele, Peluso, Emmanuele, Craciunescu, Teddy, Gelfusa, Michela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884580/
https://www.ncbi.nlm.nih.gov/pubmed/31784604
http://dx.doi.org/10.1038/s41598-019-54145-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6884580/
https://www.ncbi.nlm.nih.gov/pubmed/31784604
http://dx.doi.org/10.1038/s41598-019-54145-7

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