Reinforcement learning and neutron scattering
ORAL
Abstract
During this talk, I will discuss our recent progress with applying reinforcement learning to neutron scattering. Examples include single crystal diffraction, measurements of the order parameter, and measurement of spin-wave excitations. Our results are currently on simulated data and show that it is possible to use reinforcement learning to dramatically reduce the number of measurements required to obtain parameters from experiments. I will also discuss the advantages of incorporating physics into models
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Presenters
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William Ratcliff
GDS, National Institute of Standards and Tech, NIST
Authors
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William Ratcliff
GDS, National Institute of Standards and Tech, NIST
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Kate Meuse
Cornell University
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Jessica Opsahl-Ong
Rice University
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Paul Kienzle
National Institute of Standards and Technology, NIST Center for Neutron Research, Gaithersburg, MD, NIST, National Institute of Standards and Technology