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 …

Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather
forecasting model that provides accurate short to medium-range global predictions at …

Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …

In this article, we propose physics-informed neural operators (PINO) that combine training
data and physics constraints to learn the solution operator of a given family of parametric …

Various deep learning methodologies have recently been developed for machine condition
monitoring recently, and they have achieved impressive success in bearing fault …

Abstract Machine learning-based modeling of physical systems has experienced increased
interest in recent years. Despite some impressive progress, there is still a lack of …

The concept of a digital twin of Earth envisages the convergence of Big Earth Data with
physics-based models in an interactive computational framework that enables monitoring …

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

In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …