首页|Researchers from Argonne National Laboratory Describe Findings in Machine Learni ng (Evaluating Generalized Feature Importance Via Performance Assessment of Mach ine Learning Models for Predicting Elastic Properties of Materials)
Researchers from Argonne National Laboratory Describe Findings in Machine Learni ng (Evaluating Generalized Feature Importance Via Performance Assessment of Mach ine Learning Models for Predicting Elastic Properties of Materials)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating in Lemont, Illino is, by NewsRx journalists, research stated, “Identifying key descriptorsand und erstanding important features across different classes of materials are crucial for machine learning(ML) tools to both predict material properties and reveal t he physics underlying any process of interest.Traditionally, the predictive mod eling of elastic properties of materials is limited to only a few classes ofmat erials and a small set of ML tools despite the broad applications of these mater ials.”
LemontIllinoisUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningArgonne Na tional Laboratory