Parallel computing for differential equations has emerged as a critical field in computational science, enabling the efficient simulation of complex physical systems governed by ordinary and partial ...

This paper proposes an improved version of physics-informed neural networks (PINNs), the physics-informed kernel function neural networks (PIKFNNs), to solve various linear and some specific nonlinear ...

Abstract: As a powerful information processing tool, neural network has been widely used in the fields of computer vision, biomedicine, and oil and gas engineering, which has triggered a technological ...

This repository hosts the source code for the paper "Physical information neural network for nonlinear integro-differential equations with degenerate kernel scheme" aiming at the development of a ...

This paper proposes an efficient numerical method to obtain analytical-numerical solutions for a class of system of boundary value problems. This new algorithm is based on a reproducing kernel Hilbert ...