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 …

Fourier neural operators (FNOs) have recently been proposed as an effective framework for
learning operators that map between infinite-dimensional spaces. We prove that FNOs are …

With the increasing scale and complexity of the network, the network attack technology is
also changing, such as malicious program attack, Trojan horse, distributed denial of service …

We propose a conservative physics-informed neural network (cPINN) on discrete domains
for nonlinear conservation laws. Here, the term discrete domain represents the discrete sub …

Innovative approaches and tools play an important role in sha** design, characterization
and optimization for the field of photonics. As a subset of machine learning that learns …

In this work we investigate the possibility of using physics-informed neural networks (PINNs)
to approximate the Euler equations that model high-speed aerodynamic flows. In particular …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …

We formulate a general framework for hp-variational physics-informed neural networks (hp-
VPINNs) based on the nonlinear approximation of shallow and deep neural networks and …