In this paper, Laplace decomposition method (LDM) and Pade approximant are employed to find approximate solutions for the Whitham-Broer-Kaup shallow water model, the coupled nonlinear reaction diffusion equations and ...In this paper, Laplace decomposition method (LDM) and Pade approximant are employed to find approximate solutions for the Whitham-Broer-Kaup shallow water model, the coupled nonlinear reaction diffusion equations and the system of Hirota-Satsuma coupled KdV. In addition, the results obtained from Laplace decomposition method (LDM) and Pade approximant are compared with corresponding exact analytical solutions.展开更多
A barrier option valuation model with stochastic barrier which was regarded as the main feature of the model was developed under the Hull-White interest rate model.The purpose of this study was to deal with the stocha...A barrier option valuation model with stochastic barrier which was regarded as the main feature of the model was developed under the Hull-White interest rate model.The purpose of this study was to deal with the stochastic barrier by means of partial differential equation methods and then derive the exact analytical solutions of the barrier options.Furthermore,a numerical example was given to show how to apply this model to pricing one structured product in realistic market.Therefore,this model can provide new insight for future research on structured products involving barrier options.展开更多
地表变化是遥感领域的重点研究对象之一,掌握地表随时间演化的规律是对地观测研究中一项长期且艰巨的任务,在自然资源、生态环境、防灾减灾等诸多领域具有深远影响。然而由于地表变化驱动因子及其作用机理的复杂性,关于地表动态变化过...地表变化是遥感领域的重点研究对象之一,掌握地表随时间演化的规律是对地观测研究中一项长期且艰巨的任务,在自然资源、生态环境、防灾减灾等诸多领域具有深远影响。然而由于地表变化驱动因子及其作用机理的复杂性,关于地表动态变化过程的建模大多是简化的或局部的,较难形成完备的物理模型与数学公式表达。从方法论的角度而言,伴随着近年来大数据与AI for Science方法的快速发展,数据驱动的动态演化系统建模方法发展迅速,可以直接以观测数据序列为输入构建数据吻合度高的数据模型,作为物理模型的高保真近似甚至替代。本文梳理了3类可用于地表动态变化过程建模的数据驱动方法,即时空模态分解、主控偏微分方程反演、以及状态变量发现网络,分别利用这3类方法进行遥感图像时间序列建模,并通过时间序列影像预测评估建模精度,实验结果初步验证了数据驱动建模方法的有效性,显示了3类方法各自的特点、研究价值与应用前景。展开更多
This paper is concerned with a low-dimensional dynamical system model for analytically solving partial differential equations(PDEs).The model proposed is based on a posterior optimal truncated weighted residue(POT-WR)...This paper is concerned with a low-dimensional dynamical system model for analytically solving partial differential equations(PDEs).The model proposed is based on a posterior optimal truncated weighted residue(POT-WR)method,by which an infinite dimensional PDE is optimally truncated and analytically solved in required condition of accuracy.To end that,a POT-WR condition for PDE under consideration is used as a dynamically optimal control criterion with the solving process.A set of bases needs to be constructed without any reference database in order to establish a space to describe low-dimensional dynamical system that is required.The Lagrangian multiplier is introduced to release the constraints due to the Galerkin projection,and a penalty function is also employed to remove the orthogonal constraints.According to the extreme principle,a set of ordinary differential equations is thus obtained by taking the variational operation of the generalized optimal function.A conjugate gradient algorithm by FORTRAN code is developed to solve the ordinary differential equations.The two examples of one-dimensional heat transfer equation and nonlinear Burgers’equation show that the analytical results on the method proposed are good agreement with the numerical simulations and analytical solutions in references,and the dominant characteristics of the dynamics are well captured in case of few bases used only.展开更多
文摘In this paper, Laplace decomposition method (LDM) and Pade approximant are employed to find approximate solutions for the Whitham-Broer-Kaup shallow water model, the coupled nonlinear reaction diffusion equations and the system of Hirota-Satsuma coupled KdV. In addition, the results obtained from Laplace decomposition method (LDM) and Pade approximant are compared with corresponding exact analytical solutions.
基金National Natural Science Foundations of China(Nos.11471175,11171221)
文摘A barrier option valuation model with stochastic barrier which was regarded as the main feature of the model was developed under the Hull-White interest rate model.The purpose of this study was to deal with the stochastic barrier by means of partial differential equation methods and then derive the exact analytical solutions of the barrier options.Furthermore,a numerical example was given to show how to apply this model to pricing one structured product in realistic market.Therefore,this model can provide new insight for future research on structured products involving barrier options.
文摘地表变化是遥感领域的重点研究对象之一,掌握地表随时间演化的规律是对地观测研究中一项长期且艰巨的任务,在自然资源、生态环境、防灾减灾等诸多领域具有深远影响。然而由于地表变化驱动因子及其作用机理的复杂性,关于地表动态变化过程的建模大多是简化的或局部的,较难形成完备的物理模型与数学公式表达。从方法论的角度而言,伴随着近年来大数据与AI for Science方法的快速发展,数据驱动的动态演化系统建模方法发展迅速,可以直接以观测数据序列为输入构建数据吻合度高的数据模型,作为物理模型的高保真近似甚至替代。本文梳理了3类可用于地表动态变化过程建模的数据驱动方法,即时空模态分解、主控偏微分方程反演、以及状态变量发现网络,分别利用这3类方法进行遥感图像时间序列建模,并通过时间序列影像预测评估建模精度,实验结果初步验证了数据驱动建模方法的有效性,显示了3类方法各自的特点、研究价值与应用前景。
基金supported by Natural Science Foundation of China under Great Nos.11072053 and 11372068,and the National Basic Research Program of China under Grant No.2014CB74410.
文摘This paper is concerned with a low-dimensional dynamical system model for analytically solving partial differential equations(PDEs).The model proposed is based on a posterior optimal truncated weighted residue(POT-WR)method,by which an infinite dimensional PDE is optimally truncated and analytically solved in required condition of accuracy.To end that,a POT-WR condition for PDE under consideration is used as a dynamically optimal control criterion with the solving process.A set of bases needs to be constructed without any reference database in order to establish a space to describe low-dimensional dynamical system that is required.The Lagrangian multiplier is introduced to release the constraints due to the Galerkin projection,and a penalty function is also employed to remove the orthogonal constraints.According to the extreme principle,a set of ordinary differential equations is thus obtained by taking the variational operation of the generalized optimal function.A conjugate gradient algorithm by FORTRAN code is developed to solve the ordinary differential equations.The two examples of one-dimensional heat transfer equation and nonlinear Burgers’equation show that the analytical results on the method proposed are good agreement with the numerical simulations and analytical solutions in references,and the dominant characteristics of the dynamics are well captured in case of few bases used only.
