首页|Findings from University of Colorado Update Knowledge of Machine Learning (An Au tomated Machine-learning-assisted Stochasticfuzzy Multi-criteria Decision Makin g Tool: Addressing Record-torecord Variability In Seismic Design)
Findings from University of Colorado Update Knowledge of Machine Learning (An Au tomated Machine-learning-assisted Stochasticfuzzy Multi-criteria Decision Makin g Tool: Addressing Record-torecord Variability In Seismic Design)
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 – Current study results on Machine Learn ing have been published. According tonews originating from Boulder, Colorado, b y NewsRx correspondents, research stated, “While uncertaintyquantification (UQ) has served a prominent role in ensuring the safety of dynamical engineering sys tems,the lack of an integrated approach to handle the aleatory nature of ground motion records, a.k.a., record-to -record (RTR) variability, remains a bottlen eck in seismic design. This paper presents a novel approachwith two key feature s.”
BoulderColoradoUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesEngineeringMachine Learni ngUniversity of Colorado
NETL
NSTL
