Physics-informed neural networks(PINNs)have recently emerged as a powerful tool to solve differential equations for nonlinear mechanics.However,PINNs struggle with singular perturbation problems due to their locally a...Physics-informed neural networks(PINNs)have recently emerged as a powerful tool to solve differential equations for nonlinear mechanics.However,PINNs struggle with singular perturbation problems due to their locally abrupt behavior and singularities.The Poincaré-Lighthill-Kuo(PLK)method has been efficiently used to address these problems by applying perturbation expansions to both dependent and independent variables.This paper proposes a combination of the PLK method and PINNs,termed PLK-PINNs.The PLK-PINNs employ a parametric expression through two neural networks:one representing the mapping from parametric variables to independent variables,and the other approximating the solution of dependent variables with respect to parametric variables.Moreover,an auxiliary loss term is proposed to constrain the Jacobian determinant of the mapping within a constant sign interval to ensure the bijectivity of the mapping.The effectiveness of the proposed method is demonstrated through tests on typical singularity-shift and secular-term problems,with conventional PINNs in comparison.展开更多
This paper provides some functional equations satisfied by the generatingfunctions for enumerating general rooted planar maps with up to three parameters. Furthermore, thegenerating functions can be obtained explicitl...This paper provides some functional equations satisfied by the generatingfunctions for enumerating general rooted planar maps with up to three parameters. Furthermore, thegenerating functions can be obtained explicitly by employing the Lagrangian inversion. This is alsoan answer to an open problem in 1989.展开更多
基金supported by the National Natural Science Foundation of China Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.11988102)Lei Zhang was also supported by the National Natural Science Foundation of China(Grant No.12202451).
摘要Physics-informed neural networks(PINNs)have recently emerged as a powerful tool to solve differential equations for nonlinear mechanics.However,PINNs struggle with singular perturbation problems due to their locally abrupt behavior and singularities.The Poincaré-Lighthill-Kuo(PLK)method has been efficiently used to address these problems by applying perturbation expansions to both dependent and independent variables.This paper proposes a combination of the PLK method and PINNs,termed PLK-PINNs.The PLK-PINNs employ a parametric expression through two neural networks:one representing the mapping from parametric variables to independent variables,and the other approximating the solution of dependent variables with respect to parametric variables.Moreover,an auxiliary loss term is proposed to constrain the Jacobian determinant of the mapping within a constant sign interval to ensure the bijectivity of the mapping.The effectiveness of the proposed method is demonstrated through tests on typical singularity-shift and secular-term problems,with conventional PINNs in comparison.
基金Project 10271017 supported by National Natural Science Foundation of China
摘要This paper provides some functional equations satisfied by the generatingfunctions for enumerating general rooted planar maps with up to three parameters. Furthermore, thegenerating functions can be obtained explicitly by employing the Lagrangian inversion. This is alsoan answer to an open problem in 1989.