Solving differential equations of fractional order using an optimization technique based on training artificial neural network
M Pakdaman, A Ahmadian, S Effati… - Applied Mathematics …, 2017 - Elsevier
The current study aims to approximate the solution of fractional differential equations (FDEs)
by using the fundamental properties of artificial neural networks (ANNs) for function …
by using the fundamental properties of artificial neural networks (ANNs) for function …
Single layer Chebyshev neural network model for solving elliptic partial differential equations
S Mall, S Chakraverty - Neural Processing Letters, 2017 - Springer
The purpose of the present study is to solve partial differential equations (PDEs) using single
layer functional link artificial neural network method. Numerical solution of elliptic PDEs …
layer functional link artificial neural network method. Numerical solution of elliptic PDEs …
Multilayer perceptron neural networks with novel unsupervised training method for numerical solution of the partial differential equations
In this paper by using MultiLayer Perceptron and Radial Basis Function (RBF) neural
networks, a novel method for solving both kinds of differential equation, ordinary and partial …
networks, a novel method for solving both kinds of differential equation, ordinary and partial …
Solving partial differential equation based on extreme learning machine
In this paper, we present a novel learning method based on extreme learning machine
algorithm called ELMNET for solving partial differential equations (PDEs). A loss function …
algorithm called ELMNET for solving partial differential equations (PDEs). A loss function …
A constrained backpropagation approach for the adaptive solution of partial differential equations
This paper presents a constrained backpropagation (CPROP) methodology for solving
nonlinear elliptic and parabolic partial differential equations (PDEs) adaptively, subject to …
nonlinear elliptic and parabolic partial differential equations (PDEs) adaptively, subject to …
Solving partial differential equations using artificial neural networks
K Rudd - 2013 - search.proquest.com
This thesis presents a method for solving partial differential equations (PDEs) using articial
neural networks. The method uses a constrained backpropagation (CPROP) approach for …
neural networks. The method uses a constrained backpropagation (CPROP) approach for …
Solving the nonlinear Schrodinger equation with an unsupervised neural network
C Monterola, C Saloma - Optics Express, 2001 - opg.optica.org
We solve the nonlinear Schrodinger equation with an unsupervised neural network with the
optical axis position z and time t as inputs. The network outputs the real and imaginary …
optical axis position z and time t as inputs. The network outputs the real and imaginary …
Numerical solution of the nonlinear Schrodinger equation by feedforward neural networks
We present a method to solve boundary value problems using artificial neural networks
(ANN). A trial solution of the differential equation is written as a feed-forward neural network …
(ANN). A trial solution of the differential equation is written as a feed-forward neural network …
Solving -Body Problems with Neural Networks
M Quito Jr, C Monterola, C Saloma - Physical review letters, 2001 - APS
We show a new approach for solving the N-body problems based on neural networks.
Without loss of generality, we derived a network solution for the time-dependent positions of …
Without loss of generality, we derived a network solution for the time-dependent positions of …
Solving the nonlinear Schrödinger equation with an unsupervised neural network: estimation of error in solution
C Monterola, C Saloma - Optics communications, 2003 - Elsevier
We present a practical method for estimating the upper error bound in the neural network
(NN) solution of the nonlinear Schrödinger equation (NLSE) under different degrees of …
(NN) solution of the nonlinear Schrödinger equation (NLSE) under different degrees of …