This paper is devoted to adaptive finite element method for the nonlinear Schrödinger equation.The adaptive method is based on the extrapolation technology and a second order accurate,linear and mass preserving f...This paper is devoted to adaptive finite element method for the nonlinear Schrödinger equation.The adaptive method is based on the extrapolation technology and a second order accurate,linear and mass preserving finite element scheme.For error control,we take the difference between the numerical gradient and the recovered gradient obtained by the superconvergent cluster recovery method as the spatial discretization error estimator and the difference of numerical approximations between two consecutive time steps as the temporal discretization error estimator.A timespace adaptive algorithm is developed for numerical approximation of the nonlinear Schrödinger equation.Numerical experiments are presented to illustrate the reliability and efficiency of the proposed error estimators and the corresponding adaptive algorithm.展开更多
The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house...The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine （WCFM_SVM）. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young＇s modulus and Poisson＇s ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt （LM） algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.展开更多
The model of partially observed nonlinear system,called extended Kalman filter(EKF),and depending on some unknown parameters is considered.An approximation of the unobserved component is proposed.This approximation is...The model of partially observed nonlinear system,called extended Kalman filter(EKF),and depending on some unknown parameters is considered.An approximation of the unobserved component is proposed.This approximation is realized in two steps.First a the method of moments estimator of unknown parameter is constructed and then this estimator is substituted in the equations of extended Kalman filter.The obtained equations describe the adaptive extended Kalman filter.The properties of estimator of the unknown parameter and of the unknown state are described in the asymptotic of small noise in observations.展开更多
We consider the design of an effective and reliable adaptive finite element method(AFEM)for the nonlinear Poisson-Boltzmann equation(PBE).We first examine the two-term regularization technique for the continuous probl...We consider the design of an effective and reliable adaptive finite element method(AFEM)for the nonlinear Poisson-Boltzmann equation(PBE).We first examine the two-term regularization technique for the continuous problem recently proposed by Chen,Holst and Xu based on the removal of the singular electrostatic potential inside biomolecules;this technique made possible the development of the first complete solution and approximation theory for the Poisson-Boltzmann equation,the first provably convergent discretization and also allowed for the development of a provably convergent AFEM.However,in practical implementation,this two-term regularization exhibits numerical instability.Therefore,we examine a variation of this regularization technique which can be shown to be less susceptible to such instability.We establish a priori estimates and other basic results for the continuous regularized problem,as well as for Galerkin finite element approximations.We show that the new approach produces regularized continuous and discrete problemswith the samemathematical advantages of the original regularization.We then design an AFEM scheme for the new regularized problem and show that the resulting AFEM scheme is accurate and reliable,by proving a contraction result for the error.This result,which is one of the first results of this type for nonlinear elliptic problems,is based on using continuous and discrete a priori L¥estimates.To provide a high-quality geometric model as input to the AFEM algorithm,we also describe a class of feature-preserving adaptive mesh generation algorithms designed specifically for constructing meshes of biomolecular structures,based on the intrinsic local structure tensor of the molecular surface.All of the algorithms described in the article are implemented in the Finite Element Toolkit(FETK),developed and maintained at UCSD.The stability advantages of the new regularization scheme are demonstrated with FETK through comparisons with the original regularization approach for a model problem.The convergence and accuracy of the overall AFEMalgorithmis also illustrated by numerical approximation of electrostatic solvation energy for an insulin protein.展开更多
基金supported by NSFC Project(No.12201010)Natural Science Research Project of Higher Education in Anhui Province(No.2022AH040027)+3 种基金the Scientific Research Foundation for Scholars of Anhui Normal University(No.762135)the Research Culture Funds of Anhui Normal University(No.2022xjxm035)supported by Natural Science Research Project of Higher Education in Anhui Province(No.2022AH050205)the Scientific Research Foundation for Scholars of Anhui Normal University(No.762133).
文摘This paper is devoted to adaptive finite element method for the nonlinear Schrödinger equation.The adaptive method is based on the extrapolation technology and a second order accurate,linear and mass preserving finite element scheme.For error control,we take the difference between the numerical gradient and the recovered gradient obtained by the superconvergent cluster recovery method as the spatial discretization error estimator and the difference of numerical approximations between two consecutive time steps as the temporal discretization error estimator.A timespace adaptive algorithm is developed for numerical approximation of the nonlinear Schrödinger equation.Numerical experiments are presented to illustrate the reliability and efficiency of the proposed error estimators and the corresponding adaptive algorithm.
基金supported by the National Natural Science Foundation of China (Grant No.61373107)Wuhan Science and Technology Program, China (Grant No.2016010101010022)
文摘The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine （WCFM_SVM）. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young＇s modulus and Poisson＇s ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt （LM） algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
基金financially supported by the Russian Science Foundation research project(Grant No.24-11-00191).
文摘The model of partially observed nonlinear system,called extended Kalman filter(EKF),and depending on some unknown parameters is considered.An approximation of the unobserved component is proposed.This approximation is realized in two steps.First a the method of moments estimator of unknown parameter is constructed and then this estimator is substituted in the equations of extended Kalman filter.The obtained equations describe the adaptive extended Kalman filter.The properties of estimator of the unknown parameter and of the unknown state are described in the asymptotic of small noise in observations.
基金supported in part by NSF Awards 0715146,0821816,0915220 and 0822283(CTBP)NIHAward P41RR08605-16(NBCR),DOD/DTRA Award HDTRA-09-1-0036+1 种基金CTBP,NBCR,NSF and NIHsupported in part by NIH,NSF,HHMI,CTBP and NBCR.The third,fourth and fifth authors were supported in part by NSF Award 0715146,CTBP,NBCR and HHMI.
文摘We consider the design of an effective and reliable adaptive finite element method(AFEM)for the nonlinear Poisson-Boltzmann equation(PBE).We first examine the two-term regularization technique for the continuous problem recently proposed by Chen,Holst and Xu based on the removal of the singular electrostatic potential inside biomolecules;this technique made possible the development of the first complete solution and approximation theory for the Poisson-Boltzmann equation,the first provably convergent discretization and also allowed for the development of a provably convergent AFEM.However,in practical implementation,this two-term regularization exhibits numerical instability.Therefore,we examine a variation of this regularization technique which can be shown to be less susceptible to such instability.We establish a priori estimates and other basic results for the continuous regularized problem,as well as for Galerkin finite element approximations.We show that the new approach produces regularized continuous and discrete problemswith the samemathematical advantages of the original regularization.We then design an AFEM scheme for the new regularized problem and show that the resulting AFEM scheme is accurate and reliable,by proving a contraction result for the error.This result,which is one of the first results of this type for nonlinear elliptic problems,is based on using continuous and discrete a priori L¥estimates.To provide a high-quality geometric model as input to the AFEM algorithm,we also describe a class of feature-preserving adaptive mesh generation algorithms designed specifically for constructing meshes of biomolecular structures,based on the intrinsic local structure tensor of the molecular surface.All of the algorithms described in the article are implemented in the Finite Element Toolkit(FETK),developed and maintained at UCSD.The stability advantages of the new regularization scheme are demonstrated with FETK through comparisons with the original regularization approach for a model problem.The convergence and accuracy of the overall AFEMalgorithmis also illustrated by numerical approximation of electrostatic solvation energy for an insulin protein.
