Artificial Intelligence for Scattering Experiments | NNSS 2025 скачать в хорошем качестве

Artificial Intelligence for Scattering Experiments | NNSS 2025

Artificial Intelligence for Scattering Experiments Presented by Thomas Proffen 1st National Neutron Scattering School, 2025 Oak Ridge National Laboratory (ORNL) In this lecture, Thomas Proffen explores the role of artificial intelligence and machine learning in modern neutron scattering experiments. The presentation examines how data driven methods are transforming experiment planning, data reduction, and analysis workflows, particularly as instruments generate increasingly large and complex datasets. Proffen discusses how machine learning approaches can assist with pattern recognition, automated feature extraction, model selection, and real time decision making during experiments. The lecture highlights applications ranging from diffraction and total scattering to spectroscopy and imaging, emphasizing how AI tools can accelerate insight while complementing established physical models rather than replacing them. By integrating computational methods with scattering science, artificial intelligence offers new opportunities for adaptive experiments, improved uncertainty quantification, and more efficient use of beam time. The session underscores the importance of combining domain knowledge in neutron scattering with advances in data science to enable smarter, faster, and more reproducible research. Learn more about neutron science at ORNL: https://neutrons.ornl.gov Learn about the National Neutron Scattering School at ORNL: https://neutrons.ornl.gov/nns