Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …

This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Physics-informed neural networks (PINNs) have been popularized as a deep learning
framework that can seamlessly synthesize observational data and partial differential …

Recently, a class of machine learning methods called physics-informed neural networks
(PINNs) has been proposed and gained prevalence in solving various scientific computing …

Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

Physics-informed neural networks (PINNs) have recently emerged as promising data-driven
PDE solvers showing encouraging results on various PDEs. However, there is a …

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

In this paper, we utilise the physics-informed neural networks (PINN) combined with
interpolation polynomials to solve nonlinear partial differential equations and for simplicity …

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 …