Over the last decade, deep learning (DL), a branch of machine learning, has experienced
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …

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 paper we introduce Smooth Particle Networks (SPNets), a framework for integrating
fluid dynamics with deep networks. SPNets adds two new layers to the neural network …

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 construction of smart cities in our country has received extensive attention. Under the
situation that smart cities are vigorously promoted nowadays, compared with traditional …

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 …

In the present study, a novel application of integrated evolutionary computing paradigm is
presented for the analysis of nonlinear systems of differential equations representing the …

In this article, strength of fractional neural networks (FrNNs) is exploited to find the
approximate solutions of nonlinear systems based on Riccati equations of arbitrary order …

In this paper, we introduce a type of tensor neural network. For the first time, we propose its
numerical integration scheme and prove the computational complexity to be the polynomial …