首页|Investigators from Chongqing University of Technology Target Machine Learning (D etecting the Large-scale Wall-attached Structural Inclination Angles By a Machin e Learning Perspective In Turbulent Boundary Layer)
Investigators from Chongqing University of Technology Target Machine Learning (D etecting the Large-scale Wall-attached Structural Inclination Angles By a Machin e Learning Perspective In Turbulent Boundary Layer)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news reporting fromChongqing, People’s Republic of Chi na, by NewsRx journalists, research stated, “With the recent advancesin machine learning, strategies based on data can be used to augment wall modeling in the turbulentboundarylayer. Combined with the attached eddy hypothesis, the present work applies extreme gradientboosting (XGBoost) to predict the large-scalewall -attached structures at a range of wall-normal locationsbased on a near-wall re ference position (z®(+)approximate to 4) spanning a Reynolds-numberrange Re-ssi milar to O(10(3))-O(10(5)).”
ChongqingPeople’s Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningChongqing University of T echnology
NETL
NSTL
