Current and emerging deep-learning methods for the simulation of fluid dynamics
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 …
rapid progress. Powerful tools for tasks that have been traditionally complex to automate …
Integrated neuro-evolution-based computing solver for dynamics of nonlinear corneal shape model numerically
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 …
numerical solution of a nonlinear two-point boundary value problem arising in the modelling …
Spnets: Differentiable fluid dynamics for deep neural networks
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 …
fluid dynamics with deep networks. SPNets adds two new layers to the neural network …
Intelligent computing for numerical treatment of nonlinear prey–predator models
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 …
nonlinear prey–predator mathematical model by exploiting the strengths of integrated …
Establishment and application of intelligent city building information model based on BP neural network model
YW Li, K Cao - Computer Communications, 2020 - Elsevier
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 …
situation that smart cities are vigorously promoted nowadays, compared with traditional …
Numerical solution of doubly singular nonlinear systems using neural networks-based integrated intelligent computing
In this paper, a bio-inspired computational intelligence technique is presented for solving
nonlinear doubly singular system using artificial neural networks (ANNs), genetic algorithms …
nonlinear doubly singular system using artificial neural networks (ANNs), genetic algorithms …
A gradient-enhanced physics-informed neural network (gPINN) scheme for the coupled non-fickian/non-fourierian diffusion-thermoelasticity analysis: A novel gPINN …
K Eshkofti, SM Hosseini - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
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 …
enhanced physics-informed neural network (gPINN) with a novel structure for the …
Design of evolutionary finite difference solver for numerical treatment of computer virus propagation with countermeasures model
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 …
presented for the analysis of nonlinear systems of differential equations representing the …
Fractional neural network models for nonlinear Riccati systems
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 …
approximate solutions of nonlinear systems based on Riccati equations of arbitrary order …
Tensor neural network and its numerical integration
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 …
numerical integration scheme and prove the computational complexity to be the polynomial …