首页|Study Data from Johns Hopkins University Update Understanding of Machine Learnin g (Physics-constrained Polynomial Chaos Expansion for Scientific Machine Learnin g and Uncertainty Quantification)
Study Data from Johns Hopkins University Update Understanding of Machine Learnin g (Physics-constrained Polynomial Chaos Expansion for Scientific Machine Learnin g and Uncertainty Quantification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report.According to news originating from Baltimore, Maryland, by NewsRx c orrespondents, research stated, “We present a novel physicsconstrained polynomi al chaos expansion as a surrogate modeling method capable of performing both sci entific machine learning (SciML) and uncertainty quantification (UQ) tasks.The proposed method possesses a unique capability: it seamlessly integrates SciML in to UQ and vice versa, which allows it to quantify the uncertainties in SciML tas ks effectively and leverage SciML for improved uncertainty assessment during UQr elated tasks.”
BaltimoreMarylandUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningMathema ticsPolynomialJohns Hopkins University
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