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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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Publicat a:Sci Rep
Autors principals: Murari, Andrea, Lungaroni, Michele, Peluso, Emmanuele, Craciunescu, Teddy, Gelfusa, Michela
Format: Artigo
Idioma:Inglês
Publicat: Nature Publishing Group UK 2019
Matèries:
Accés en línia:https://ncbi.nlm.nih.gov/pmc/articles/PMC6884580/
https://ncbi.nlm.nih.gov/pubmed/31784604
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-019-54145-7
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