Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Physics-informed neural networks (PINNs) for fluid mechanics: A review
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 …
numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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 …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Physics-informed neural networks for heat transfer problems
Physics-informed neural networks (PINNs) have gained popularity across different
engineering fields due to their effectiveness in solving realistic problems with noisy data and …
engineering fields due to their effectiveness in solving realistic problems with noisy data and …
Physics-informed neural networks for inverse problems in supersonic flows
Accurate solutions to inverse supersonic compressible flow problems are often required for
designing specialized aerospace vehicles. In particular, we consider the problem where we …
designing specialized aerospace vehicles. In particular, we consider the problem where we …
Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Parallel physics-informed neural networks via domain decomposition
We develop a distributed framework for the physics-informed neural networks (PINNs)
based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs …
based on two recent extensions, namely conservative PINNs (cPINNs) and extended PINNs …
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
Physics-informed neural networks (PINNs) are successful machine-learning methods for the
solution and identification of partial differential equations. We employ PINNs for solving the …
solution and identification of partial differential equations. We employ PINNs for solving the …
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm
for solving problems relating to differential equations. Compared to classical numerical …
for solving problems relating to differential equations. Compared to classical numerical …
Dense velocity reconstruction from particle image velocimetry/particle tracking velocimetry using a physics-informed neural network
The velocities measured by particle image velocimetry (PIV) and particle tracking
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …
velocimetry (PTV) commonly provide sparse information on flow motions. A dense velocity …