首页|Researchers from Texas State University Report on Findings in Machine Learning ( Uncertainty Quantification in CO [ [2] ] Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Mode l Using Representative Geological Realizations and Unsupervised ...)
Researchers from Texas State University Report on Findings in Machine Learning ( Uncertainty Quantification in CO [ [2] ] Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Mode l Using Representative Geological Realizations and Unsupervised ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from San Marcos, Texas, by NewsRx editors, the research stated, “Evaluating uncertainty in CO 2 injection projections often requires numerous high-resolution geological realizations (GRs ) which, although effective, are computationally demanding. This study proposes the use of representative geological realizations (RGRs) as an efficient approac h to capture the uncertainty range of the full set while reducing computational costs.”
Texas State UniversitySan MarcosTexa sUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMac hine Learning
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