首页|New Machine Learning Findings Reported from University of Wis- consin Madison (Machine Learning for Interpreting Coherent X-ray Speckle Patterns)
New Machine Learning Findings Reported from University of Wis- consin Madison (Machine Learning for Interpreting Coherent X-ray Speckle Patterns)
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NETL
NSTL
Elsevier
Data detailed on Machine Learning have been presented. According to news originating from Madison, Wisconsin, by NewsRx correspondents, research stated, "Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging." Funders for this research include Laboratory Directed Research and Development (LDRD), United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE), United States Department of Energy (DOE). Our news journalists obtained a quote from the research from the University of Wisconsin Madison, "Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent Xray speckle patterns according to the disk number density in the corresponding structure."
MadisonWisconsinUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Wisconsin Madison
NETL
NSTL
Elsevier
