A Reduced Basis Method for the Nonlinear Poisson-Boltzmann Equation

被引:7
作者
Ji, Lijie [1 ]
Chen, Yanlai [2 ]
Xu, Zhenli [3 ,4 ]
机构
[1] Shanghai Jiao Tong Univ, Sch Math Sci, Shanghai 200240, Peoples R China
[2] Univ Massachusetts Dartmouth, Dept Math, 285 Old Westport Rd, N Dartmouth, MA 02747 USA
[3] Shanghai Jiao Tong Univ, Minist Educ, Sch Math Sci, Inst Nat Sci, Shanghai 200240, Peoples R China
[4] Shanghai Jiao Tong Univ, Minist Educ, Key Lab Sci & Engn Comp, Shanghai 200240, Peoples R China
基金
美国国家科学基金会;
关键词
order modeling; reduced basis method; Poisson-Boltzmann equation; differential capacitance; POSTERIORI ERROR ESTIMATION; BASIS APPROXIMATION; EMPIRICAL INTERPOLATION; ELEMENT; ELECTROSTATICS; SURFACES; BOUNDS; MODEL;
D O I
10.4208/aamm.OA-2018-0188
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In numerical simulations of many charged systems at the micro/nano scale, a common theme is the repeated resolution of the Poisson-Boltzmann equation. This task proves challenging, if not entirely infeasible, largely due to the nonlinearity of the equation and the high dimensionality of the physical and parametric domains with the latter emulating the system configuration. In this paper, we for the first time adapt a mathematically rigorous and computationally efficient model order reduction paradigm, the so-called reduced basis method (RBM), to mitigate this challenge. We adopt a finite difference method as the mandatory underlying scheme to produce the high-fidelity numerical solutions of the Poisson-Boltzmann equation upon which the fast RBM algorithm is built and its performance is measured against. Numerical tests presented in this paper demonstrate the high efficiency and accuracy of the fast algorithm, the reliability of its error estimation, as well as its capability in effectively capturing the boundary layer.
引用
收藏
页码:1200 / 1218
页数:19
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