首页|Recent Studies from University of Illinois Add New Data to Machine Learning (Ran domized Physics-informed Machine Learning for Uncertainty Quantification In High -dimensional Inverse Problems)
Recent Studies from University of Illinois Add New Data to Machine Learning (Ran domized Physics-informed Machine Learning for Uncertainty Quantification In High -dimensional Inverse Problems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learn ing have been published. According to newsreporting out of Urbana, Illinois, by NewsRx editors, research stated, “We propose the randomized physics-informed co nditional Karhunen-Lo & egrave;ve expansion (rPICKLE) method for u ncertainty quantificationin high-dimensional inverse problems. In rPICKLE, the states and parameters of the governing partialdifferential equation (PDE) are a pproximated via truncated conditional Karhunen-Lo & egrave;ve expansions (cKLEs).”
UrbanaIllinoisUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Illinois
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