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 …

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

Physics-informed neural networks (PINNs) and their variants have been very popular in
recent years as algorithms for the numerical simulation of both forward and inverse …

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date PINNs have not been successful in simulating dynamical systems whose solution …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

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

Partial differential equations (PDEs) play a fundamental role in the mathematical modelling
of many processes and systems in physical, biological and other sciences. To simulate such …

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

This paper explores challenges in training Physics-Informed Neural Networks (PINNs),
emphasizing the role of the loss landscape in the training process. We examine difficulties in …

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