A physics-based approach to structural health monitoring (SHM) has practical shortcomings
which restrict its suitability to simple structures under well controlled environments. With the …

Training machine learning tools such as neural networks require the availability of sizable
data, which can be difficult for engineering and scientific applications where experiments or …

Robust modeling of a multimodal dynamic system is a challenging and fast-growing area of
research. In this study, an integrated bi-modal computing paradigm based on Nonlinear …

In this paper, a new method based on single layer Legendre Neural Network (LeNN) model
has been developed to solve initial and boundary value problems. In the proposed …

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

We propose a new model-based unsupervised learning method, called VarNet, for the
solution of partial differential equations (PDEs) using deep neural networks. Particularly, we …

The advent of technology including big data has allowed machine learning technology to
strengthen its place in solving different science and engineering complex problems …

Data sparsity is a common issue to train machine learning tools such as neural networks for
engineering and scientific applications, where experiments and simulations are expensive …

In the present study, stochastic numerical computing approach is developed by applying
artificial neural networks (ANNs) to compute the solution of Lane–Emden type boundary …

The objective of this paper is to solve second order non-linear ordinary differential equations
of Lane–Emden type using Chebyshev Neural Network (ChNN) model. These equations are …