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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2019
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| Materias: | |
| 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 |