In this paper, we propose an artificial neural network model (ANN) to solve a partial differential equation (PDE) constrained optimization problem. Here, the discretize then optimize approach is used. At first, the Legendre polynomials are used to discretize the optimization problem and transform it into a quadratic optimization problem with linear constraint. Then an ANN model is proposed to solve the obtained quadratic optimization problem. Finally, several examples are presented to illustrate the abilities and efficiency of the proposed approach
Ordinary Differential Equations (ODEs) play a key role in describing the physical, chemical, and bio...
Indeed, interesting properties of artificial neural networks approach made this non-parametric model...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...
This paper develops a novel neural network (NN) based near optimal boundary control scheme for distr...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
Recent works have shown that neural networks can be employed to solve partial differential equations...
Abstract. In this paper, a swarm intelligence technique, better known as Particle swarm optimization...
Partial differential equations (PDEs) are essential mathematical models for describing a wide range ...
In this paper, the influence of the optimization algorithms Adam, RMSprop, L-BFGS and SGD with momen...
This paper presents a novel recurrent time continuous neural network model which performs nonlinear ...
In this paper, we introduce a novel approach based on modified artificial neural network and optimiz...
We present a method to solve initial and boundary value problems using artificial neural networks. A...
Recent works have shown that deep neural networks can be employed to solve partial differential equa...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
Recent works have shown that deep neural networks can be employed to solve partial differential equa...
Ordinary Differential Equations (ODEs) play a key role in describing the physical, chemical, and bio...
Indeed, interesting properties of artificial neural networks approach made this non-parametric model...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...
This paper develops a novel neural network (NN) based near optimal boundary control scheme for distr...
The subject of this thesis is an application of artificial neural networks to solving linear and non...
Recent works have shown that neural networks can be employed to solve partial differential equations...
Abstract. In this paper, a swarm intelligence technique, better known as Particle swarm optimization...
Partial differential equations (PDEs) are essential mathematical models for describing a wide range ...
In this paper, the influence of the optimization algorithms Adam, RMSprop, L-BFGS and SGD with momen...
This paper presents a novel recurrent time continuous neural network model which performs nonlinear ...
In this paper, we introduce a novel approach based on modified artificial neural network and optimiz...
We present a method to solve initial and boundary value problems using artificial neural networks. A...
Recent works have shown that deep neural networks can be employed to solve partial differential equa...
This paper presents an efficient approach based on a recurrent neural network for solving constraine...
Recent works have shown that deep neural networks can be employed to solve partial differential equa...
Ordinary Differential Equations (ODEs) play a key role in describing the physical, chemical, and bio...
Indeed, interesting properties of artificial neural networks approach made this non-parametric model...
This paper is concerned with utilizing neural networks and analog circuits to solve constrained opti...