首页|Researchers at University of Queensland Target Machine Learning (Optimizing Pers istent Currents In a Ring-shaped Bose-einstein Condensate Using Machine Learning )
Researchers at University of Queensland Target Machine Learning (Optimizing Pers istent Currents In a Ring-shaped Bose-einstein Condensate Using Machine Learning )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news reporting originating from St. Lucia, Australia, by NewsRx correspondents, research stated, "We demonstrate a method for generat ing persistent currents in Bose-Einstein condensates by using a Gaussian process learner to experimentally control the stirring of the superfluid. The learner o ptimizes four different outcomes of the stirring process: (O.I) targeting and (O .II) maximization of the persistent current winding number and (O.III) targeting and (O.IV) maximization with time constraints." Our news editors obtained a quote from the research from the University of Queen sland, "The learner optimizations are determined based on the achieved winding n umber and the number of spurious vortices introduced by stirring. We find that t he learner is successful in optimizing the stirring protocols, although the opti mal stirring profiles vary significantly depending strongly on the choice of cos t function and scenario."
St. LuciaAustraliaAustralia and New ZealandBose-einsteinCyborgsEmerging TechnologiesMachine LearningPhysic sUniversity of Queensland
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