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Dataset: Capturing dynamical correlations using implicit neural representations

Authors: Sathya Chitturi; Zhurun Ji; Alex Petsch; Chunjing Jia; Youssef Nashed; Josh Turner;

Dataset: Capturing dynamical correlations using implicit neural representations

Abstract

This work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0022216, as well as under Contract DE-AC0276SF00515 for the Materials Sciences and Engineering Division and the Linac Coherent Light Source (LCLS). A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. J. J. Turner acknowledges support from the U.S. DOE, Office of Science, Basic Energy Sciences through the Early Career Research Program. Z. Ji is supported by the Stanford Science fellowship, and the Urbenek-Chodorow postdoctoral fellowship awards. A.N. Petsch acknowledges funding and support from the Engineering and Physical Sciences Research Council (EPSRC) under Grant Nos. EP/L015544/1 and EP/R011141/1.

Dataset of Linear Spin Wave Theory simulations accompanying the manuscript: Capturing dynamical correlations using implicit neural representations.

Keywords

Machine Learning, Linear Spin Wave Theory, Neutron Scattering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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