Physics-informed neural networks (PINNs) have received significant attention as a
representative deep learning-based technique for solving partial differential equations …

The Internet of Things (IoT) is an innovative technology that presents effective and attractive
solutions to revolutionize various domains. Numerous solutions based on the IoT have been …

Physics-informed neural networks (PINNs) incorporate established physical principles into
the training of deep neural networks, ensuring that they adhere to the underlying physics of …

This paper proposes a novel framework for simulating the dynamics of beams on elastic
foundations. Specifically, partial differential equations modeling Euler–Bernoulli and …

The Industry 4.0 is aimed at improving production efficiency and flexibility using automation
and data exchange technologies, wherein all participants and components must cooperate …

The challenge of sustainably producing goods and services for healthy living on a healthy
planet requires simultaneous consideration of economic, societal, and environmental …

In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge
Platform for Smart Manufacturing through implementation of four instructive real‐world use …

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving
partial differential equations (PDEs) in various scientific and engineering applications …

One of the most important parameters in the performance of wireless communication
systems is the channel estimation. However, apart from the usual OFDM modes, there are …

Physics-informed learning methods have gained significant attention as a function
approximator for solving partial differential equation problems. However, the vanilla PINN …