Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …

We present PFNN, a penalty-free neural network method, to efficiently solve a class of
second-order boundary-value problems on complex geometries. To reduce the smoothness …

Computer simulations are used to model of complex physical systems. Often, these models
represent the solutions (or at least approximations) to partial differential equations that are …

In this paper, we introduce a new method based on Bernstein Neural Network model
(BeNN) and extreme learning machine algorithm to solve the differential equation. In the …

In this study, a machine learning approach based on the unsupervised version of wavelet
neural networks (WNNs) is used to solve two-dimensional elliptic partial differential …

A new penalty-free neural network method, PFNN-2, is presented for solving partial
differential equations, which is a subsequent improvement of our previously proposed PFNN …

In this work, an intelligent computing algorithm is developed for finding the approximate
solution of heart model based on nonlinear Van der Pol (VdP)-type second-order ordinary …

In this paper new computational intelligence techniques have been developed for the
nonlinear magnetohydrodynamics (MHD) Jeffery–Hamel flow problem using three different …

We present a neuro-heuristic computing platform for finding the solution for initial value
problems (IVPs) of nonlinear pantograph systems based on functional differential equations …

In this article, a simple and efficient approach for the approximate solution of a nonlinear
differential equation known as Troesch's problem is proposed. In this article, a mathematical …