首页|Findings from University of Arizona Provide New Insights into Machine Learning ( Advancing Battery Safety: Integrating Multiphysics and Machine Learning for Ther mal Runaway Prediction In Lithiumion Battery Module)
Findings from University of Arizona Provide New Insights into Machine Learning ( Advancing Battery Safety: Integrating Multiphysics and Machine Learning for Ther mal Runaway Prediction In Lithiumion Battery Module)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting originating from Tucson, Arizona, by NewsRx correspondents, research stated, “The safety concerns associated with lithium-ion batteries (LIBs) have led to the development of a novel framework co mbining advanced machine learning (ML) techniques with multiphysics modeling. He rein, we report an ML framework aiming to predict the occurrence of thermal runa way (TR) in the LIB module by employing a multiphysics model that incorporates t hermal, electrochemical, and degradation sub-models.”
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