Initial Boundary Value Conditions Research Articles

Triple Mixed Integral Transformation and Applications for Initial-Boundary Value Problems

In this paper, we establish a new integral transformation method for solving the initial-boundary value problems of the partial differential equations. The method is a combined expression mainly based on the triple integral transformation of Laplace and Sumudu. We study the basic properties of the triple Laplace-Sumudu transform, and give the triple Laplace-Sumudu transforms of some basic functions. And as applications, we solve some heat flow equations and wave equations with initial-boundary value conditions by using the method. The results show that the triple Laplace-Sumudu transform is more efficient and useful to deal with these problems.

Thermoviscoelastic response of skin tissue via fractional Pennes bioheat and fractional viscoelasticity

In this paper, the thermoviscoelastic response of skin tissue, as a typical class of viscoelastic biological tissue, subject to thermal shock is studied based on the fractional Kelvin-Voigt model and fractional Pennes bioheat conduction. A time-fractional biothermoviscoelastic governing equation is derived and initial-boundary value conditions are given for monolayer skin tissue of finite thickness. The temperature change and thermal stress of viscoelastic skin tissue are obtained by the Laplace transform and its inversion using matlab software. The influences of viscosity coefficient, relaxation times, fractional-order viscosity/heat parameter, and blood perfusion rate of skin tissue on the temperature distribution and the elastic displacement are analyzed. The numerical results show that fractional-order viscosity does not affect temperature, whereas fractional-order heat flow does. Moreover, fractional-order heat flow affects the sensitivity of blood perfusion rate to the thermodynamic response.

Data-driven wave solutions of (2+1)-dimensional nonlinear dispersive long wave equation by deep learning

In this paper, we introduce a deep learning framework to solve the (2+1)-dimensional nonlinear dispersive long wave equation. Using it, we study the data-driven solitary wave solutions, periodic wave solutions, kink wave solutions and various multiple soliton solutions for the equations with initial–boundary value conditions. On the one hand, we obtain the data-driven traveling wave solutions of the equations, which have high accuracy compared with corresponding exact traveling wave solutions. On the other hand, we set more complicated initial–boundary value problems of the equations, whose exact traveling wave solution cannot be easy to obtain, but our network still works well and gives its numerical solution. The results show that our deep learning algorithm is able to accurately capture the dynamical behavior of traveling waves of the (2+1)-dimensional nonlinear dispersive long wave equation.

ASYMPTOTIC STABILITY FOR A VISCOELASTIC EQUATION WITH THE TIME-VARYING DELAY

The goal of the present paper is to study the viscoelastic wave equation with the time-varying delay under initial-boundary value conditions. By using the multiplier method together with some properties of the convex functions, the explicit and general stability results of the total energy are proved under the general assumption on the relaxation function g.

Energy decay and blow-up of solutions for a class of system of generalized nonlinear Klein-Gordon equations with source and damping terms

In this work, we investigate generalized coupled nonlinear Klein-Gordon equations with nonlinear damping and source terms and initial-boundary value conditions, in a bounded domain. We obtain decay of solutions by use of Nakao inequality. The blow up of solutions with negative initial energy is also established.

Data-driven forward-inverse problems for Yajima–Oikawa system using deep learning with parameter regularization

We investigate data-driven forward-inverse problems for Yajima–Oikawa (YO) system by employing two technologies to improve the performance of deep physics-informed neural network (PINN), namely neuron-wise locally adaptive activation functions and L2 norm parameter regularization. Indeed, we not only recover three different forms of vector rogue waves (RWs) under three distinct initial–boundary value conditions in the forward problem of YO system, including bright–bright RWs, intermediate–bright RWs and dark–bright RWs, but also study the inverse problem of YO system by using training data with different noise intensity. In order to deal with the problem that the capacity of learning unknown parameters is not ideal as utilizing training data with noise interference in the PINN with only locally adaptive activation functions, thus we introduce L2 norm regularization, which can drive the weights closer to origin, into PINN with locally adaptive activation functions. Then we find that the PINN model with two strategies shows amazing training effect by using training data with noise interference to investigate the inverse problem of YO system.

New semi-analytical solutions of the time-fractional Fokker–Planck equation by the neural network method

This paper suggests a neural network method for solving the time-fractional Fokker–Planck equation. An energy function is constructed by means of initial–boundary value conditions. The Caputo derivative is approximated by L1 numerical scheme and an unconstrained discretization minimization problem is presented. Gradient descent algorithm is adopted for neural network training. Semi-analytical solutions are obtained where two numerical examples demonstrate the method’s efficiency.

Approximate solution of initial boundary value problems for ordinary differential equations with fractal derivative

<abstract><p>Fractal ordinary differential equations are successfully established by He's fractal derivative in a fractal space, and their variational principles are obtained by semi-inverse transform method.Taylor series method is used to solve the given fractal equations with initial boundary value conditions, and sometimes <italic>Ying Buzu</italic> algorithm play an important role in this process. Examples show the Taylor series method and <italic>Ying Buzu</italic> algorithm are powerful and simple tools.</p></abstract>

Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning

In the field of mathematical physics, there exist many physically interesting nonlinear dispersive equations with peakon solutions, which are solitary waves with discontinuous first-order derivative at the wave peak. In this paper, we apply the multi-layer physics-informed neural networks (PINNs) deep learning to successfully study the data-driven peakon and periodic peakon solutions of some well-known nonlinear dispersion equations with initial–boundary value conditions such as the Camassa–Holm (CH) equation, Degasperis–Procesi equation, modified CH equation with cubic nonlinearity, Novikov equation with cubic nonlinearity, mCH-Novikov equation, b-family equation with quartic nonlinearity, generalized modified CH equation with quintic nonlinearity, and etc. Moreover, we also study the data-driven parameter discovery of the CH equation with the aid of the single peakon These results will be useful to further study the peakon solutions and corresponding experimental design of nonlinear dispersive equations.

Research on Deep Neural Network Solution of Ordinary Differential Equations and Its Application

This article discusses the application of a single hidden layer neural network algorithm in solving numerical ordinary differential equations. Set the approximate solution with neural network structure to meet the initial boundary value conditions of ordinary differential equations, and discretize the weights of neural networks in the original equations. The optimization problem is transformed into the training problem of the neural network in the approximate solution, so that the approximate solution is close to the real solution. The changes in the structure, weights, and thresholds of the neural network are observed through network deduction. Numerical examples based on the python language prove the algorithm's performance Effectiveness.

NEW EXISTENCE RESULTS FOR NONLINEAR FRACTIONAL JERK EQUATIONS WITH INITIAL-BOUNDARY VALUE CONDITIONS AT RESONANCE

<abstract> In this paper, a novel jerk system involving fractional-order-derivat-ives is proposed. We obtain the existence of solutions of nonlinear fractional jerk differential equations with initial-boundary value conditions at resonance by coincidence degree theory. This paper enriches some existing literatures. Finally, we present an example to illustrate our main results. </abstract>

The Maximum Principle for Variable-Order Fractional Diffusion Equations and the Estimates of Higher Variable-Order Fractional Derivatives

In this paper, the maximum principle of variable-order fractional diffusion equations and the estimates of fractional derivatives with higher variable order are investigated. Firstly, we deduce the fractional derivative of a function of higher variable order at an arbitrary point. We also give an estimate of the error. Some important inequalities for fractional derivatives of variable order at arbitrary points and extreme points are presented. Then, the maximum principles of Riesz-Caputo fractional differential equations in terms of the multi-term space-time variable order are proved. Finally, under the initial-boundary value conditions, it is verified via the proposed principle that the solutions are unique, and their continuous dependance holds.

Another way of solving a free boundary problem related to DCIS model

In this paper, we consider a free boundary problem related to ductal carcinoma in situ (DCIS) model. Existence and uniqueness theorem is proved by using Banach fixed point theorem. For computation part, the decomposition method of Adomian is first implemented to get an approximate solution of intermediate function , which satisfies a nonlinear equation with initial boundary value conditions, and then based on relationships between and , numerical solutions of free boundary are obtained. The approximate solution can be straightly derived by applying Adomian decomposition method to the linear equation involving . Finally, a numerical example is presented to show the validity and applicability of our method.

Maximum principle and its application to multi-index Hadamard fractional diffusion equation

This study establishes some new maximum principle which will help to investigate an IBVP for multi-index Hadamard fractional diffusion equation. With the help of the new maximum principle, this paper ensures that the focused multi-index Hadamard fractional diffusion equation possesses at most one classical solution and that the solution depends continuously on its initial boundary value conditions.

The finite volume method for two-dimensional Burgers’ equation

The Burgers' equation as a useful mathematical model is applied in many fields such as fluid dynamic, heat conduction, and continuous stochastic processes. Although this equation can be solved analytically, it is only based on the restricted set of initial-boundary value conditions. So the numerical method for solving the Burgers' equation is necessary. In this paper, a finite volume method is presented for the two-dimensional Burgers' equation on a triangular mesh. We give a semi-discrete finite volume scheme and drive the optimal error estimate in H1 norm.

Numerical solution of Volterra partial integro-differential equations based on sinc-collocation method

We provide the numerical solution of a Volterra integro-differential equation of parabolic type with memory term subject to initial boundary value conditions. Finite difference method in combination with product trapezoidal integration rule is used to discretize the equation in time and sinc-collocation method is employed in space. A weakly singular kernel has been viewed as an important case in this study. The convergence analysis has been discussed in detail, which shows that the approach exponentially converges to the solution. Furthermore, numerical examples and illustrations are presented to prove the validity of the suggested method.

A mixed-finite volume element coupled with the method of characteristic fractional step difference for simulating transient behavior of semiconductor device of heat conductor and its numerical analysis

The mathematical system is formulated by four partial differential equations combined with initialboundary value conditions to describe transient behavior of three-dimensional semiconductor device with heat conduction. The first equation of an elliptic type is defined with respect to the electric potential, the successive two equations of convection dominated diffusion type are given to define the electron concentration and the hole concentration, and the fourth equation of heat conductor is for the temperature. The electric potential appears in the equations of electron concentration, hole concentration and the temperature in the formation of the intensity. A mass conservative numerical approximation of the electric potential is presented by using the mixed finite volume element, and the accuracy of computation of the electric intensity is improved one order. The method of characteristic fractional step difference is applied to discretize the other three equations, where the hyperbolic terms are approximated by a difference quotient in the characteristics and the diffusion terms are discretized by the method of fractional step difference. The computation of three-dimensional problem works efficiently by dividing it into three one-dimensional subproblems and every subproblem is solved by the method of speedup in parallel. Using a pair of different grids (coarse partition and refined partition), piecewise threefold quadratic interpolation, variation theory, multiplicative commutation rule of differential operators, mathematical induction and priori estimates theory and special technique of differential equations, we derive an optimal second order estimate in L2-norm. This numerical method is valuable in the simulation of semiconductor device theoretically and actually, and gives a powerful tool to solve the international problem presented by J. Douglas, Jr.

A Numerical Approximation Structured by Mixed Finite Element and Upwind Fractional Step Difference for Semiconductor Device with Heat Conduction and Its Numerical Analysis

AbstractA coupled mathematical system of four quasi-linear partial differential equations and the initial-boundary value conditions is presented to interpret transient behavior of three dimensional semiconductor device with heat conduction. The electric potential is defined by an elliptic equation, the electron and hole concentrations are determined by convection-dominated diffusion equations and the temperature is interpreted by a heat conduction equation. A mixed finite element approximation is used to get the electric field potential and one order of computational accuracy is improved. Two concentration equations and the heat conduction equation are solved by a fractional step scheme modified by a second-order upwind difference method, which can overcome numerical oscillation, dispersion and computational complexity. This changes the computation of a three dimensional problem into three successive computations of one-dimensional problem where the method of speedup is used and the computational work is greatly shortened. An optimal second-order error estimate in L2 norm is derived by prior estimate theory and other special techniques of partial differential equations. This type of parallel method is important in numerical analysis and is most valuable in numerical application of semiconductor device and it can successfully solve this international famous problem.

Random Attractors for the Kirchhoff-Type Suspension Bridge Equations with Strong Damping and White Noises

In this paper, we investigate the existence of random attractor for the random dynamical system generated by the Kirchhoff-type suspension bridge equations with strong damping and white noises. We first prove the existence and uniqueness of solutions to the initial boundary value conditions, and then we study the existence of the global attractors of the equation.

Numerical method of mixed finite volume-modified upwind fractional step difference for three-dimensional semiconductor device transient behavior problems

Transient behavior of three-dimensional semiconductor device with heat conduction is described by a coupled mathematical system of four quasi-linear partial differential equations with initial-boundary value conditions. The electric potential is defined by an elliptic equation and it appears in the following three equations via the electric field intensity. The electron concentration and the hole concentration are determined by convection-dominated diffusion equations and the temperature is interpreted by a heat conduction equation. A mixed finite volume element approximation, keeping physical conservation law, is used to get numerical values of the electric potential and the accuracy is improved one order. Two concentrations and the heat conduction are computed by a fractional step method combined with second-order upwind differences. This method can overcome numerical oscillation, dispersion and decreases computational complexity. Then a three-dimensional problem is solved by computing three successive one-dimensional problems where the method of speedup is used and the computational work is greatly shortened. An optimal second-order error estimate in L2 norm is derived by using prior estimate theory and other special techniques of partial differential equations. This type of mass-conservative parallel method is important and is most valuable in numerical analysis and application of semiconductor device.