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 …

Despite the significant progress over the last 50 years in simulating flow problems using
numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate …

Neural operators can learn nonlinear mappings between function spaces and offer a new
simulation paradigm for real-time prediction of complex dynamics for realistic diverse …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Abstract We present Graph Neural Diffusion (GRAND) that approaches deep learning on
graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as …

Accurate battery life prediction is a critical part of the business case for electric vehicles,
stationary energy storage, and nascent applications such as electric aircraft. Existing …

In the last 50 years there has been a tremendous progress in solving numerically the Navier-
Stokes equations using finite differences, finite elements, spectral, and even meshless …

Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for
solving forward and inverse problems of nonlinear partial differential equations (PDEs). By …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …