首页|Studies from Jilin Jianzhu University Have Provided New Information about Machin e Learning (Bayesian Hybrid-kernel Machinelearning- assisted Sensitivity Analysi s and Sensitivity-relevant Inverse Modeling for Groundwater Dnapl Contamination)
Studies from Jilin Jianzhu University Have Provided New Information about Machin e Learning (Bayesian Hybrid-kernel Machinelearning- assisted Sensitivity Analysi s and Sensitivity-relevant Inverse Modeling for Groundwater Dnapl Contamination)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Machine Learn ing. According to news reporting originating from Changchun, People’s Republic o f China, by NewsRx correspondents, research stated, “Accurate source characteriz ation and transport parameter estimation is important when seeking to predict th e spatiotemporal distribution of dense non-aqueous phase liquid (DNAPL) contamin ants in groundwater. However, this is a complex multimodal search problem prone to equifinality and premature convergence, which leads to considerable error.” Funders for this research include National Natural Science Foundation of China ( NSFC), Science and Technology Research Project of Jilin Provincial Education Dep artment.
ChangchunPeople’s Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningMathematicsNumerical Mo delingSwarm IntelligenceJilin Jianzhu University
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