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Implementing Machine Learning in the PCWG Tool

• Authors : Stuart, Peter

• Source: Conference: Presented at the Power Curve Working Group Meeting, 13 December 2016, Glasgow, Scotland

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Machine learning strategies for systems with invariance properties

• Authors : Templeton, Jeremy [Sandia National Lab. (SNL-CA), Livermore, CA (United States)]

• Source: Journal of Computational Physics; 318; C

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Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

• Authors : Schlepütz, Christian [Paul Scherrer Inst. (PSI), Villigen (Switzerland). Swiss Light Source]

• Source: Nanotechnology; 27; 37

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Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence

• Authors : Aoun, Bachir [Argonne National Lab. (ANL), Lemont, IL (United States)]

• Source: Journal of Computational Chemistry; 37; 12

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Fault Modeling of Extreme Scale Applications Using Machine Learning

• Authors : Hoisie, Adolfy [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)]

• Source: Parallel and Distributed Processing Symposium, 2016 IEEE International; Conference: 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Chicago, IL (United States), 23-27

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Machine Learning for Massive Scale Cosmology. Final Report

• Authors : Schneider, Jeff [Carnegie Mellon Univ., Pittsburgh, PA (United States)]

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Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

• Authors : Tkatchenko, Alexandre [Max-Planck-Gesellschaft, Berlin (Germany)]

• Source: Journal of Physical Chemistry Letters; 6; 12

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Using Machine Learning to Create Turbine Performance Models (Presentation)

• Authors : Clifton, A.

• Source: Conference: Presented at the Power Curve Working Group, 12 March 2013, Brande, Denmark; Other Information: NREL (National Renewable Energy Laboratory)

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Machine Learning for Big Data: A Study to Understand Limits at Scale

• Authors : Del-Castillo-Negrete, Carlos [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)]

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Theoretical Neuroscience in the Age of Big Data and Machine Learning.

• Authors : Chance, Frances

• Source: Conference: Proposed for presentation at the Advancing Neuroscience with the National Labs held May 18-19, 2017 in Chicago, IL.

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