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机译:使用嵌套采样和稀疏多项式混沌代理的地下流模型的有效贝叶斯推断
Center for Subsurface Modeling, Institute for Computational Engineering and Sciences,The University of Texas at Austin, 201 East 24th Street, Campus Mail C0200, Austin, TX 78712, USA,Institute of Petroleum Engineering (IPE), Heriot-Watt University, Edinburgh Campus, Edinburgh, EH14 1AS, Scotland, UK;
Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia;
Center for Subsurface Modeling (CSM), Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX, USA;
Subsurface flow models; Nested sampling; Polynomial chaos expansion; Sparsity promoting regularization; Least Angle Regression;
机译:一种新的代理建模方法,将多项式混沌扩展和高斯内核在稀疏的贝叶斯学习框架中结合
机译:贝叶斯弹性问题的多模型多项式混沌替代字典
机译:使用稀疏多项式混沌展开法替代具有复杂结构的储层模型的加速采样
机译:用于基准问题的多保真度,梯度增强和局部优化的稀疏多项式混沌和Kriging替代模型
机译:结合动态和稀疏模型的贝叶斯推理:在3D电生理成像中的应用
机译:使用嵌套采样的系统生物学中的贝叶斯模型比较和参数推断
机译:基于回归的稀疏多项式混沌,用于地下流量模型的不确定量化