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Machine Learning creates new correlations for particles in flows

A new step in understanding particle behavior in density-stratified interfaces has been made through the use of machine learning. We gathered unprecedented data from a particle settling experiment and used it to train their models, revealing key correlations and dependence on dimensionless parameters. The study is not yet accepted, take it with a grain of salt https://arxiv.org/abs/2302.02242


This study has the potential to revolutionize industrial applications and improve our ability to predict particle motion in these systems. #MachineLearning #ParticleMotion #StratifiedInterfaces

Thanks to the team: Danny, Aviv, Dmitry for their help with the experiments. Kudos to Liron for completing this great work, and the main credit goes to Dr. Teddy Lazebnik

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New paper has been accepted (24-May-2024)

Congratulations to Dr. Ivan Litvinov and co-authors for the new work on the novel piezoresistive sensing flow and/or temperature sensor based on MEMS bifurcation sensors, being accepted to Applied Phy

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