Abstract
New research on Machine Learning is th e subject of a report. According to news reporting originating in Lyon, France, by NewsRx journalists, research stated, "We present a data-driven pipeline for m odel building that combines interpretable machine learning, hydrodynamic theorie s, and microscopic models. The goal is to uncover the underlying processes gover ning nonlinear dynamics experiments." The news reporters obtained a quote from the research from University Lyon, "We exemplify our method with data from microfluidic experiments where crystals of s treaming droplets support the propagation of nonlinear waves absent in passive c rystals. By combining physics-inspired neural networks, known as neural operator s, with symbolic regression tools, we infer the solution, as well as the mathema tical form, of a nonlinear dynamical system that accurately models the experimen tal data. Finally, we interpret this continuum model from fundamental physics pr inciples."
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