首页|New Findings in Machine Learning Described from Johns Hopkins University (Physics-agnostic and Physics-infused Machine Learning for Thin Films Flows: Modelling, and Predictions From Small Data)
New Findings in Machine Learning Described from Johns Hopkins University (Physics-agnostic and Physics-infused Machine Learning for Thin Films Flows: Modelling, and Predictions From Small Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning are presented in a new report. According to newsreporting from Baltimore, Maryland, by NewsRx journalists, research stated, “Numerical simulations ofmultiphase flows are crucial in numerous engineering applications, but are often limited by the computationallydemanding solution of the Navier-Stokes (NS) equations. The development of surrogate modelsrelies on involved algebra and several assumptions.”
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