We use the physics-informed neural network to solve a variety of femtosecond optical soliton
solutions of the high-order nonlinear Schrödinger equation, including one-soliton solution …

The physics-informed neural networks (PINNs) can be used to deep learn the nonlinear
partial differential equations and other types of physical models. In this paper, we use the …

Deep learning methods for image analysis are attracting increasing interest for application in
a wide range of different research fields. Here we aim to systematically analyze and discuss …

Prior and well-grounded produces evaluation is vital in quantifying a well and financial
assessment at the field level for discovering agricultural commodity strategic action plans for …

In this paper, we investigate the forward problems on the data-driven rational solitons for the
(2+ 1)-dimensional Kadomtsev–Petviashvili-I (KP-I) equation and spin-nonlinear …

This paper proposes an intelligent hybrid model that leverages machine learning and
artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by …

Monitoring and forecasting hospitalization rates are of essential significance to public health
systems in understanding and managing overall healthcare deliveries and strategizing long …

In recent years, significant research efforts have been dedicated to developing solutions for
nonlinear partial differential equations (PDEs) with applications in physics. Among these …

We introduce a deep neural network learning scheme to discover the Bäcklund transforms
(BTs) of soliton evolution equations and an enhanced deep learning scheme for data-driven …

Based on the physics informed neural networks (PINNs), two types of supervised deep
learning problems are explored. On the one hand, the data-driven vortex solutions of the 2D …