摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于机器学习的新报告。根据NewsRx记者从中国长春发回的新闻报道，研究表明：“在寻求预测地下水中致密非水相液体(DNAPL)含量的时空分布时，准确的源特征和输运参数估计非常重要，但这是一个复杂的多模态搜索问题，容易出现等效性和早熟收敛，导致相当大的误差。”本研究的资助单位包括国家自然科学基金(NSFC)、吉林省教育厅科技研究项目。
Abstract
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.
NETL
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