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Vladimir Starostin

Showing results (1-10 of 12) with videos related to

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Journal of Applied Crystallography|August 7, 2024
On the analysis of two-time correlation functions: equilibrium versus non-equilibrium systemsAnastasia Ragulskaya, Vladimir Starostin, Fajun Zhang, et al.
Science Advances|March 14, 2025
Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimationVladimir Starostin, Maximilian Dax, Alexander Gerlach, et al.
Journal of Applied Crystallography|February 13, 2023
Machine learning for scattering data: strategies, perspectives and applications to surface scatteringAlexander Hinderhofer, Alessandro Greco, Vladimir Starostin, et al.
Journal of Applied Crystallography|April 10, 2024
Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledgeValentin Munteanu, Vladimir Starostin, Alessandro Greco, et al.
Journal of Applied Crystallography|December 5, 2019
Fast fitting of reflectivity data of growing thin films using neural networksAlessandro Greco, Vladimir Starostin, Christos Karapanagiotis, et al.
Journal of Applied Crystallography|April 2, 2025
Benchmarking deep learning for automated peak detection on GIWAXS dataConstantin Völter, Vladimir Starostin, Dmitry Lapkin, et al.
Journal of Applied Crystallography|May 2, 2022
Neural network analysis of neutron and X-ray reflectivity data: automated analysis using <i>mlreflect</i>, experimental errors and feature engineeringAlessandro Greco, Vladimir Starostin, Evelyn Edel, et al.
Iucrj|July 18, 2022
Reverse-engineering method for XPCS studies of non-equilibrium dynamicsAnastasia Ragulskaya, Vladimir Starostin, Nafisa Begam, et al.
Journal of Applied Crystallography|June 3, 2026
Towards machine-learning-based on-the-fly analysis of neutron reflectometryAnne Rentzsch, Valentin Munteanu, Oliver Odira Anyanor, et al.
Journal of Synchrotron Radiation|October 18, 2023
Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environmentsLinus Pithan, Vladimir Starostin, David Mareček, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Journal of Applied Crystallography|August 7, 2024
On the analysis of two-time correlation functions: equilibrium versus non-equilibrium systemsAnastasia Ragulskaya, Vladimir Starostin, Fajun Zhang, et al.
Science Advances|March 14, 2025
Fast and reliable probabilistic reflectometry inversion with prior-amortized neural posterior estimationVladimir Starostin, Maximilian Dax, Alexander Gerlach, et al.
Journal of Applied Crystallography|February 13, 2023
Machine learning for scattering data: strategies, perspectives and applications to surface scatteringAlexander Hinderhofer, Alessandro Greco, Vladimir Starostin, et al.
Journal of Applied Crystallography|April 10, 2024
Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledgeValentin Munteanu, Vladimir Starostin, Alessandro Greco, et al.
Journal of Applied Crystallography|December 5, 2019
Fast fitting of reflectivity data of growing thin films using neural networksAlessandro Greco, Vladimir Starostin, Christos Karapanagiotis, et al.
Journal of Applied Crystallography|April 2, 2025
Benchmarking deep learning for automated peak detection on GIWAXS dataConstantin Völter, Vladimir Starostin, Dmitry Lapkin, et al.
Journal of Applied Crystallography|May 2, 2022
Neural network analysis of neutron and X-ray reflectivity data: automated analysis using <i>mlreflect</i>, experimental errors and feature engineeringAlessandro Greco, Vladimir Starostin, Evelyn Edel, et al.
Iucrj|July 18, 2022
Reverse-engineering method for XPCS studies of non-equilibrium dynamicsAnastasia Ragulskaya, Vladimir Starostin, Nafisa Begam, et al.
Journal of Applied Crystallography|June 3, 2026
Towards machine-learning-based on-the-fly analysis of neutron reflectometryAnne Rentzsch, Valentin Munteanu, Oliver Odira Anyanor, et al.
Journal of Synchrotron Radiation|October 18, 2023
Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environmentsLinus Pithan, Vladimir Starostin, David Mareček, et al.
Pageof 2