We present a method of discovering governing differential equations from data without the
need to specify a priori the terms to appear in the equation. The input to our method is a …

In this paper, we present an innovative and powerful combination of grammatical evolution
and a physics-informed neural network approach for symbolically solving the Lane-Emden …

Symbolic regression methods simultaneously determine the model functional form and the
regression parameter values by generating expression trees. Symbolic regression can …

Abstract The fractional generalized Bagley–Torvik equation (FGB-TE) is a mathematical
description of the motion of an immersed plate in a Newtonian fluid. The analytical study of …

Abstract Error-Correcting Output Codes (ECOC) is widely used in the field of multiclass
classification. As an optimal codematrix is key to the performance of an ECOC algorithm, this …

The stochastic small disturbance stability is widely recognized as a major issue in the small
signal stability of power system due to complex stochastic factors such as increasing large …

Relying on artificial bee colony programming (ABCP), we present in this paper, for the first
time, a novel methodology for solving differential equations. The three-phase evolving …

The present study aimed to use artificial intelligence to obtain a mathematical model to
approximate the exact solution for linear and nonlinear ordinary differential equations with …

Stochastic differential equations (SDE) have wide applications in natural phenomena,
engineering, finance, and biological models. Obtaining analytic solutions for an SDE is often …

nalysis of structural dynamics is of fundamental importance to countless engineering
applications. Analyses in both research and industrial settings have traditionally relied on …