首页|New Findings in Machine Learning Described from German Research Center for Geosc ience (GFZ) (Capturing Directivity In Probabilistic Seismic Hazard Analysis for New Zealand: Challenges, Implications, and a Machine Learning Approach for ...)
New Findings in Machine Learning Described from German Research Center for Geosc ience (GFZ) (Capturing Directivity In Probabilistic Seismic Hazard Analysis for New Zealand: Challenges, Implications, and a Machine Learning Approach for ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Potsdam, Germany, by N ewsRx journalists, research stated, “The proximity of fast -slipping crustal fau lts to urban areas may result in pulse -like ground motions from rupture directi vity, which can contribute to increased levels of damage even for engineered str uctures. Systematic modeling of directivity within probabilistic seismic hazard analysis (PSHA) remains challenging to implement at the regional scale, despite the availability of directivity models in the literature.”
PotsdamGermanyEuropeCyborgsEmerg ing TechnologiesMachine LearningGerman Research Center for Geoscience (GFZ)
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