This article presents a nonlinear solver combining regression analysis and a multiscale simulation scheme. First, the proposed method repeats microscopic analysis of a local simulation domain, which is extracted from the entire global domain, to statistically estimate the relation(s) between the value of a dependent variable at a point and values at surrounding points. The relation is called regression function. Subsequent global analysis reveals the behavior of the global domain with only coarse-grained points using the regression function quickly at low computational cost, which can be accomplished using a multiscale numerical solver, called the seamless-domain method. The objective of the study is to solve a nonlinear problem accurately and at low cost by combining the 2 techniques. We present an example problem of a nonlinear steadystate heat conduction analysis of a heterogeneous material. The proposed model using fewer than 1000 points generates a solution with precision similar to that of a standard finite-element solution using hundreds of thousands of nodes. To investigate the relationship between the accuracy and computational time, we apply the seamless-domain method under varying conditions such as the number of iterations of the prior analysis for statistical data learning.KEYWORDS elliptic, multiscale, nonlinear solvers, partial differential equations, regression analysis
| INTRODUCTIONPrevious work 1-4 presented a multiscale numerical solver called the seamless-domain method (SDM). The SDM model is meshless and represented by only a small number of coarse-grained points (CPs). The method constructs a structure as a "seamless" analytical domain whose distributions of a dependent variable and its gradient are almost continuous Nomenclature: Symbol, Explanation; # −1 , inverse of #; # T , transpose of #; Ω G ⊂ R d , domain for global analysis; Ω L ⊂ R d , domain for local analysis; Ω R ⊂ Ω L , region of influence; Γ G , boundary of the global domain; Γ L , boundary of the local domain; Γ R , boundary of the region of influence; a ∈ R 1 × m , weighting coefficient matrix; d ∈ {1, … , 3}, dimension of domain; f i , i-th response-surface function that determines the dependent-variable value at a coarse-grained point (CP) from the values at surrounding CPs (u R m ð Þ ); f L i , i-th response-surface function that determines u L m ð Þ from u R m ð Þ ; m, number of CPs in a region of influence; m l , number of iterations of the prior local analysis for constructing each response surface; m r , number of response surfaces; n, number of CPs in a global domain; N ∈ R 1 × m , interpolating function matrix for u R m ð Þ at temperature u ref ; R, set of all real numbers; u(x) ∈ R, dependent variable at point x; u G n ð Þ ∈R n , dependent variable of CPs in a global domain (Ω G ); u L m ð Þ ∈R m , dependent variable for all CPs near a local domain 0 s boundary (Γ L ); u R m ð Þ ∈R m , dependent variable for all CPs near a boundary of a region of influence (Γ R ); u ref i , reference temperature for CP i; x ∈ R d ,...