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 …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

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

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 …

Characterizing internal structures and defects in materials is a challenging task, often
requiring solutions to inverse problems with unknown topology, geometry, material …

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

Failure trajectories, probable failure zones, and damage indices are some of the key
quantities of relevance in brittle fracture mechanics. High-fidelity numerical solvers that …

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 …

We propose a new approach to the solution of the wave propagation and full waveform
inversions (FWIs) based on a recent advance in deep learning called physics‐informed …