For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

Purpose This study aims to explore the evolving field of physics-informed neural networks
(PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) …

Physics-informed neural networks (PINNs), which are promising tools for solving nonlinear
equations in the absence of labeled data, have been successfully applied for continuum …

Pore structures provide extra freedoms for the design of porous media, leading to desirable
properties, such as high catalytic rate, energy storage efficiency, and specific strength. This …

Sea surface temperature (SST) prediction has attracted increasing attention, due to its
crucial role in understanding the Earth's climate and ocean system. Existing SST prediction …

Physics informed neural network (PINN) method is proposed to alleviate problems in many
science and engineering scenarios when data-collection is difficult, or traditional numerical …

In practical engineering applications, there is a high demand for inverting parameters for
various materials, and obtaining monitoring data can be costly. Traditional inverse methods …

Methodologies inspired by physics-informed neural networks (PINNs) were used to forecast
observations recorded by stationary ocean buoys. We combined buoy observations with …

Abstract Physics-informed Neural Network (PINN) has been introduced recently to predict
and understand complex physical phenomena by directly incorporating feedback from …

This paper proposes a 3D point cloud segmentation algorithm based on a depth camera for
large-scale model point cloud unsupervised class segmentation. The algorithm utilizes …