首页|New Findings Reported from Sandia National Laboratories DescribeAdvances in Mac hine Learning (Boosting Barlow Twins ReducedOrder Modeling for Machine Learning -based Surrogate Models In Multiphase Flow Problems)
New Findings Reported from Sandia National Laboratories DescribeAdvances in Mac hine Learning (Boosting Barlow Twins ReducedOrder Modeling for Machine Learning -based Surrogate Models In Multiphase Flow Problems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Machine Learning have been presented. According to news originatingfrom Albuquerque, New Mexico, by NewsRx correspondents, research stated, “We present an innovativeapproach calle d boosting Barlow Twins reduced order modeling (BBT-ROM) to enhance the reliabil ity ofmachine learning surrogate models for multiphase flow problems. BBT-ROM b uilds upon Barlow Twinsreduced order modeling that leverages self-supervised le arning to effectively handle linear and nonlinearmanifolds by constructing well -structured latent spaces of input parameters and output quantities.”
AlbuquerqueNew MexicoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSan dia National Laboratories
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