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 …

Physics-informed neural networks (PINNs) have shown to be effective tools for solving both
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …

Physics-informed neural networks (PINNs) have gained popularity across different
engineering fields due to their effectiveness in solving realistic problems with noisy data and …

Physics-informed neural networks (PINNs) have received significant attention as a
representative deep learning-based technique for solving partial differential equations …

Here we propose a generalized space-time domain decomposition approach for the physics-
informed neural networks (PINNs) to solve nonlinear partial differential equations (PDEs) on …

Accurate solutions to inverse supersonic compressible flow problems are often required for
designing specialized aerospace vehicles. In particular, we consider the problem where we …

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

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