This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to o...This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.展开更多
Stochastic dynamic analysis of the nonlinear system is an open research question which has drawn many scholars'attention for its importance and challenge.Fokker–Planck–Kolmogorov(FPK)equation is of great signifi...Stochastic dynamic analysis of the nonlinear system is an open research question which has drawn many scholars'attention for its importance and challenge.Fokker–Planck–Kolmogorov(FPK)equation is of great significance because of its theoretical strictness and computational accuracy.However,practical difficulties with the FPK method appear when the analysis of multi-degree-offreedom(MDOF)with more general nonlinearity is required.In the present paper,by invoking the idea of equivalence of probability flux,the general high-dimensional FPK equation related to MDOF system is reduced to one-dimensional FPK equation.Then a cell renormalized method(CRM)which is based on the numerical reconstruction of the derived moments of FPK equation is introduced by coarsening the continuous state space into a discretized region of cells.Then the cell renormalized FPK(CR-FPK)equation is solved by difference method.Three numerical examples are illustrated and the effectiveness of proposed method is assessed and verified.展开更多
基金supported by JSPS KAKENHI (Grants 17K06633 and 18K18898)
文摘This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set.
文摘Stochastic dynamic analysis of the nonlinear system is an open research question which has drawn many scholars'attention for its importance and challenge.Fokker–Planck–Kolmogorov(FPK)equation is of great significance because of its theoretical strictness and computational accuracy.However,practical difficulties with the FPK method appear when the analysis of multi-degree-offreedom(MDOF)with more general nonlinearity is required.In the present paper,by invoking the idea of equivalence of probability flux,the general high-dimensional FPK equation related to MDOF system is reduced to one-dimensional FPK equation.Then a cell renormalized method(CRM)which is based on the numerical reconstruction of the derived moments of FPK equation is introduced by coarsening the continuous state space into a discretized region of cells.Then the cell renormalized FPK(CR-FPK)equation is solved by difference method.Three numerical examples are illustrated and the effectiveness of proposed method is assessed and verified.
