Physics-informed neural networks (PINNs) have shown to be effective tools for solving both
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …

Physics-informed neural networks (PINNs) as a means of solving partial differential
equations (PDE) have garnered much attention in the Computational Science and …

Physics-informed neural networks (PINNs) have emerged as an effective technique for
solving PDEs in a wide range of domains. It is noticed, however, that the performance of …

The paper presents an efficient and robust data-driven deep learning (DL) computational
framework developed for linear continuum elasticity problems. The methodology is based on …

Recent work in scientific machine learning (SciML) has focused on incorporating partial
differential equation (PDE) information into the learning process. Much of this work has …

Physics-informed neural networks (PINNs) are an emerging method for solving partial
differential equations (PDEs) and have been widely applied in the field of scientific …

Building reduced-order models (ROMs) is essential for efficient forecasting and control of
complex dynamical systems. Recently, autoencoder-based methods for building such …

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving the
forward and inverse problems involving partial differential equations (PDEs). The method …

The emerging physics informed neural network (PINN) has been recently applied to a wide
range of mathematical problems. It is promising to precisely solve the partial differential …

In order to offer guidelines for physics-informed neural network (PINN) implementation, this
study presents a comprehensive review of PINN, an emerging field at the intersection of …