Robotics & Machine Learning Daily News2024,Issue(Dec.16) :13-14.

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)

Robotics & Machine Learning Daily News2024,Issue(Dec.16) :13-14.

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)

扫码查看

Abstract

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).”

Key words

Urbana/Illinois/United States/North a nd Central America/Cyborgs/Emerging Technologies/Machine Learning/University of Illinois

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
段落导航相关论文