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 …

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

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 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 …

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

Abstract Physics-Informed Neural Networks (PINNs) and extended PINNs (XPINNs) have
emerged as a promising approach in computational science and engineering for solving …

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

We propose to enhance the training of physics-informed neural networks. To this aim, we
introduce nonlinear additive and multiplicative preconditioning strategies for the widely used …