Since the term “Fuzzy differential equations”(FDEs) emerged in the literature in 1978,
prevailing research effort has been dedicated not only to the development of the concepts …

Modeling of uncertainty differential equations is very important issue in applied sciences and
engineering, while the natural way to model such dynamical systems is to use fuzzy …

The intension of the present work is to present the stochastic numerical approach for solving
human immunodeficiency virus (HIV) infection model of cluster of differentiation 4 of T-cells …

In this study, bio-inspired computational techniques have been exploited to get the
numerical solution of a nonlinear two-point boundary value problem arising in the modelling …

In this study, a new computing paradigm is presented for evaluation of dynamics of
nonlinear prey–predator mathematical model by exploiting the strengths of integrated …

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 …

Since neural networks have universal approximation capabilities, therefore it is possible to
postulate them as solutions for given differential equations that define unsupervised errors …

In this paper, a novel meta-heuristic computing solver is presented for solving the singular
three-point second-order boundary value problems using artificial neural networks (ANNs) …

In this paper, a bio-inspired computational intelligence technique is presented for solving
nonlinear doubly singular system using artificial neural networks (ANNs), genetic algorithms …

This paper proposes a modified artificial intelligence (AI) approach based on the gradient-
enhanced physics-informed neural network (gPINN) with a novel structure for the …