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Found 9 papers, 0 papers with code
An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations
The recent introduction of the Least-Squares Support Vector Regression (LS-SVR) algorithm for solving differential and integral equations has sparked interest.
Accelerating Fractional PINNs using Operational Matrices of Derivative
This paper presents a novel operational matrix method to accelerate the training of fractional Physics-Informed Neural Networks (fPINNs).
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy
We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images.
deepFDEnet: A Novel Neural Network Architecture for Solving Fractional Differential Equations
The primary goal of this research is to propose a novel architecture for a deep neural network that can solve fractional differential equations accurately.
Solving Falkner-Skan type equations via Legendre and Chebyshev Neural Blocks
The efficiency of the proposed method is carried out by simulating various configurations of the Falkner-Skan equation.
A least squares support vector regression for anisotropic diffusion filtering
Anisotropic diffusion filtering for signal smoothing as a low-pass filter has the advantage of the edge-preserving, i. e., it does not affect the edges that contain more critical data than the other parts of the signal.
Legendre Deep Neural Network (LDNN) and its application for approximation of nonlinear Volterra Fredholm Hammerstein integral equations
In this paper, we propose Legendre Deep Neural Network (LDNN) for solving nonlinear Volterra Fredholm Hammerstein integral equations (VFHIEs).
Legendre Deep Neural Network (LDNN) and its application for approximation of nonlinear Volterra–Fredholm–Hammerstein integral equations
In particular, Volterra–Fredholm–Hammerstein integral equations are the main type of these integral equations and researchers are interested in investigating and solving these equations.
Symbolically Solving Partial Differential Equations using Deep Learning
We describe a neural-based method for generating exact or approximate solutions to differential equations in the form of mathematical expressions.
