首页|Charles University Researchers Highlight Recent Research in Machine Learning (Ma gnetopause location modeling using machine learning: inaccuracy due to solar win d parameter propagation)
Charles University Researchers Highlight Recent Research in Machine Learning (Ma gnetopause location modeling using machine learning: inaccuracy due to solar win d parameter propagation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news originating from Charles University b y NewsRx correspondents, research stated, "An intrinsic limitation of empirical models of the magnetopause location is a predefined magnetopause shape and assum ed functional dependences on relevant parameters."Our news reporters obtained a quote from the research from Charles University: " We overcome this limitation using a machine learning approach (artificial neural networks), allowing us to incorporate general, purely data-driven dependences. For the training and testing of the developed neural network model, a data set o f about 15,000 magnetopause crossings identified in the THEMIS A-E, Magion 4, Ge otail, and Interball-1 satellite data in the subsolar region is used. A cylindri cal symmetry around the direction of the impinging solar wind is assumed, and so lar wind dynamic pressure, interplanetary magnetic field magnitude, cone angle, clock angle, tilt angle, and corrected Dst index are considered as parameters. T he effect of these parameters on the magnetopause location is revealed. The perf ormance of the developed model is compared with other empirical magnetopause mod els. Finally, we demonstrate and discuss the inaccuracy of magnetopause models d ue to the inaccurate information about the impinging solar wind parameters based on measurements near the L1 point."
Charles UniversityCyborgsEmerging Te chnologiesMachine Learning
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