The trade-off between more user bandwidth and quality of service requirements introduces
unprecedented challenges to the next generation smart optical networks. In this regard, the …

One of the main challenges of quantum many‐body physics is the exponential growth in the
dimensionality of the Hilbert space with system size. This growth makes solving the …

Physics-informed neural networks (PINNs) have been proposed to learn the solution of
partial differential equations (PDE). In PINNs, the residual form of the PDE of interest and its …

Abstract Machine learning (ML) methods have recently been increasingly widely used in
quantum chemistry. While ML methods are now accepted as high accuracy approaches to …

The pandemic has shown us that it is quite important to keep track record our health digitally.
And at the same time, it also showed us the great potential of Instruments like wearable …

Quantum machine learning is at the intersection of two of the most sought after research
areas—quantum computing and classical machine learning. Quantum machine learning …

The purpose of the present study is to solve partial differential equations (PDEs) using single
layer functional link artificial neural network method. Numerical solution of elliptic PDEs …

This paper explores the difficulties in solving partial differential equations (PDEs) using
physics-informed neural networks (PINNs). PINNs use physics as a regularization term in …

A recent method for solving differential equations using feedforward neural networks was
applied to a non-steady fixed bed non-catalytic solid–gas reactor. As neural networks have …

In this study, stochastic computational techniques are developed for the solution of boundary
value problems (BVPs) of second order Pantograph functional differential equation (PFDE) …