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- W4317861010 abstract "Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community." @default.
- W4317861010 created "2023-01-25" @default.
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- W4317861010 date "2023-02-01" @default.
- W4317861010 modified "2023-10-14" @default.
- W4317861010 title "Machine learning for scattering data: strategies, perspectives and applications to surface scattering" @default.
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- W4317861010 doi "https://doi.org/10.1107/s1600576722011566" @default.
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