非线性多端网络理论

作为对非线性网络研究上的一个尝试,在文中引入了多端网络理论,研究限于具有恒定通量的非线性网络。得到了关于非线性N端,2n端,p+1端,2(p+1)端网络的两个基本性质。     

论具有恒定通量的复杂非线性网络的试探解法

本文将网络变换法推广于解任意复杂的具有恒定通量的非线性网络,在应用这种方法时,从网络几何的观点上研究了寻求最佳解案的问题,并获得了一些初步结果。在附录1中将王显荣建议的解法——克希荷夫方程法和本文介绍的网络变换法作了一些比较。在附录2中,研究了其中包含有可分出来的线性网络部份的非线性网络,得到了当这种网络包含有任意多个非线性元件时的解案。     

非线性欧拉方程组的完美匹配层吸收边界条件

对于非线性Euler方程,提出一类基于完美匹配层(PML)技术的吸收边界条件。首先对线性化的Euler方程设计出PML公式,然后将线性化Euler方程中的通量函数替换成相对应的非线性通量函数,得到非线性的PML方程。考虑到PML方程中包含有一个刚性的源项,文中采用一种隐显Runge-Kutta方法来求解空间半离散后得到的ODE系统。数值实验表明设计的非线性PML吸收边界条件优于传统的特征边界条件。     

JFNK方法在S N输运计算中的应用

JFNK(Jacobian-free Newton-Krylov)方法是一种求解非线性方程的高效迭代算法.传统输运计算中的负通量修正与k-特征值迭代使得原本线性的输运计算转变为非线性问题数值求解.为提高非线性输运问题的计算效率,将这两类非线性问题离散成残差形式的非线性方程组,并采用JFNK方法对其进行迭代求解.分析不同...     

球几何中子输运保正线性间断有限元格式

针对球几何中子输运方程线性间断有限元方法计算的负中子通量问题,构造了保正线性间断有限元格式,该格式保持中子角通量0阶矩和1阶矩。现有方法计算中子角通量非负时,采用传统的线性间断有限元方法,求解线性方程组;原方法计算出现负通量,则采用构造的保正格式,求解非线性方程组。编制了球几何中子输运问题保正格式程序模块,并集成到应用程序。数值算例表明构造的保正格式计算的中子通量非负,有效降低数值误差,提高数值计算的精度。     

激光脉冲放大器增益通量的广义变分迭代解法

研究一个激光脉冲放大器增益通量的模型. 利用广义变分迭代方法, 首先决定Lagrange乘子,然后构造迭代关系式. 最后得到了相应模型近似解. 关键词： 非线性 变分迭代 激光脉冲放大器     

一类含有复杂电阻的n阶三角形网络的等效电阻公式

研究了一类n阶三角形电阻网络的等效电阻.采用建构等效模型的方法建立了二阶非线性差分方程模型,采用等效变换的方法给出了非线性差分方程的解,获得了n阶三角形电阻网络的等效电阻公式,并对所得结论进行了分析与比较.最后我们提出了一个有待解决的深刻问题.     

考虑频散效应的一维非线性地震波数值模拟

非线性理论是解决地学问题的重要手段, 充分认识非线性波动特征有助于解释实际观测资料中的一些特殊地震现象. 本文基于Hokstad改造的非线性本构方程, 利用交错网格有限差分法实现了固体介质中一维非线性地震波数值模拟; 采用通量校正传输方法消除非线性数值模拟中波形振荡, 提高模拟精度. 通过与解析解的对比验证了模拟结果的正确性. 研究结果显示了非线性系数对地震波的传播有重要影响, 并且当取适当的非线性和频散系数时, 地震波表现出孤立波的传播特性. 最后分析了不同的非线性地震波在固体介质中的传播演化特征.     

基于二阶格式的有限体积保正格式

从二阶线性格式出发,通过对法向通量进行重构,得到非线性两点通量,获得四面体网格上的单元中心型有限体积保正格式.该格式适用于求解间断和各向异性扩散系数问题.无需假设辅助未知量非负,避免了辅助未知量计算出负时"遇负置零"的人为处理方式;并且证明该格式在每个非线性Picard迭代步具有强保正性,即当源项和边界条件非负时,线性化格式的非平凡解是严格大于零的.数值算例验证该格式具有二阶收敛性且是保正的.     

非线性耦合超混沌R(o)ssler系统和网络的同步

研究两个通过非线性函数对称耦合的超混沌Roessler系统的同步问题．通过对超混沌系统的线性项与非线性项的适当分离,构造一个特殊的非线性函数,作为耦合函数,发现在耦合强度α=0．5附近的一小段区域里存在稳定的超混沌同步现象．利用线性系统的稳定性分析准则和条件Lyapunov指数来检验同步状态的稳定性,并进一步研究了由多个超混沌Roessler系统单元通过非线性函数按照完全连接形式组成的网络的混沌同步问题。显示许多耦合单元组成的网络,满足同步稳定性的耦合强度的取值范围可以仅从2个单元组成的网络的参数取值范围估计到。此外发现耦合强度的值与耦合单元数量成反比,数值模拟结果证实所提出方法对超混沌系统和网络的混沌同步是有效的。     

沿非线性界面传输的TM波传播常数的解析解

本文得到了沿一线性介质与非线性介质界面传输的ＴＭ波精确的色散关系和传播常数的解析计算公式，导出了计算非线性介质中传输功率流的积分公式。本文方法计算传播常数及功率流的优点是可不必先知道电场分布。     

Iterative methods for solving nonlinear problems of nuclear reactor criticality

The paper presents iterative methods for calculating the neutron flux distribution in nonlinear problems of nuclear reactor criticality. Algorithms for solving equations for variations in the neutron flux are considered. Convergence of the iterative processes is studied for two nonlinear problems in which macroscopic interaction cross sections are functionals of the spatial neutron distribution. In the first problem, the neutron flux distribution depends on the water coolant density, and in the second one, it depends on the fuel temperature. Simple relationships connecting the vapor content and the temperature with the neutron flux are used.     

论复杂磁路和非线性直流电路的普遍解法

一、前言 复杂磁路和非线性电路的求解自来是个难题。1952年克鲁格等著的“电工原理”一书曾经明确指出,复杂的非线性电路的一般解法直到现在还未被研究出来(见该书第五章第六节)。过了一个时期,非线性电路的解法,在以戴维南定理为根据的“取出电动势法”的基础上,已有进一步的发展(见1954年聶孟著“电工学底理论基础”第六章§§36—39)。但适用于含有任意多个非线性元件的直流电路的一般解法似还未被提出来。至于磁路,由于并无类似的戴维南定理可资利用,目前所能解决的似只限于含有三个支路的情形(能     

Soliton,breather, and rogue wave solutions for solving the nonlinear Schrödinger equation using a deep learning method with physical constraints

The nonlinear Schro¨dinger equation is a classical integrable equation which contains plenty of significant properties and occurs in many physical areas.However,due to the difficulty of solving this equation,in particular in high dimensions,lots of methods are proposed to effectively obtain different kinds of solutions,such as neural networks among others.Recently,a method where some underlying physical laws are embeded into a conventional neural network is proposed to uncover the equation’s dynamical behaviors from spatiotemporal data directly.Compared with traditional neural networks,this method can obtain remarkably accurate solution with extraordinarily less data.Meanwhile,this method also provides a better physical explanation and generalization.In this paper,based on the above method,we present an improved deep learning method to recover the soliton solutions,breather solution,and rogue wave solutions of the nonlinear Schro¨dinger equation.In particular,the dynamical behaviors and error analysis about the one-order and two-order rogue waves of nonlinear integrable equations are revealed by the deep neural network with physical constraints for the first time.Moreover,the effects of different numbers of initial points sampled,collocation points sampled,network layers,neurons per hidden layer on the one-order rogue wave dynamics of this equation have been considered with the help of the control variable way under the same initial and boundary conditions.Numerical experiments show that the dynamical behaviors of soliton solutions,breather solution,and rogue wave solutions of the integrable nonlinear Schro¨dinger equation can be well reconstructed by utilizing this physically-constrained deep learning method.     

A high performance neural network for solving nonlinear programming problems with hybrid constraints

A continuous neural network is proposed in this Letter for solving optimization problems. It not only can solve nonlinear programming problems with the constraints of equality and inequality, but also has a higher performance. The main advantage of the network is that it is an extension of Newton's gradient method for constrained problems, the dynamic behavior of the network under special constraints and the convergence rate can be investigated. Furthermore, the proposed network is simpler than the existing networks even for solving positive definite quadratic programming problems. The network considered is constrained by a projection operator on a convex set. The advanced performance of the proposed network is demonstrated by means of simulation of several numerical examples.     

Symmetric behavior of vortex states of superconducting networks in a magnetic field

We present magnetic field dependence of phase transition temperature and vortex configuration of superconducting networks based on theoretical study. The applied magnetic field is called “filling field” that is defined by applied magnetic flux (in unit of the flux quantum) per unit loop of the superconducting network. If a superconducting network is composed of very thin wires whose thicknesses are less than coherence length, the de Gennes–Alexander (dGA) theory is applicable. We have already shown that field dependences of transition temperature curves have symmetric behavior about the filling field of 1/2 by solving the dGA equation numerically in square lattices, honeycomb lattices, cubic lattices and those with randomly lack of wires networks. Many experimental studies also show the symmetric behavior. In this paper, we make an explicit theoretical explanation of symmetric behaviors of superconducting network respect to the applied field.     

A dual neural network for convex quadratic programming subject to linear equality and inequality constraints

A recurrent neural network called the dual neural network is proposed in this Letter for solving the strictly convex quadratic programming problems. Compared to other recurrent neural networks, the proposed dual network with fewer neurons can solve quadratic programming problems subject to equality, inequality, and bound constraints. The dual neural network is shown to be globally exponentially convergent to optimal solutions of quadratic programming problems. In addition, compared to neural networks containing high-order nonlinear terms, the dynamic equation of the proposed dual neural network is piecewise linear, and the network architecture is thus much simpler. The global convergence behavior of the dual neural network is demonstrated by an illustrative numerical example.     

A physics-constrained deep residual network for solving the sine-Gordon equation

Despite some empirical successes for solving nonlinear evolution equations using deep learning,there are several unresolved issues.First,it could not uncover the dynamical behaviors of some equations where highly nonlinear source terms are included very well.Second,the gradient exploding and vanishing problems often occur for the traditional feedforward neural networks.In this paper,we propose a new architecture that combines the deep residual neural network with some underlying physical laws.Using the sine-Gordon equation as an example,we show that the numerical result is in good agreement with the exact soliton solution.In addition,a lot of numerical experiments show that the model is robust under small perturbations to a certain extent.