A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

In this work, we consider a high-order discretization of compressible viscous flows allowing
parallelization both in space and time. The discontinuous Galerkin spectral element method …

Physics-informed neural networks have emerged as an alternative method for solving partial
differential equations. However, for complex problems, the training of such networks can still …

In this study, the parallel inverted dual time stepping (PIDTS) method has been investigated
for analyzing turbomachinery unsteady flows. This is the first known effort in exploiting …

Water drainage modeling codes need to be updated with parallel simulation capability to
leverage modern computational trends. Parallelized finite-difference/volume simulation of …

Simulating unsteady fluid flows always leads to huge computational costs, and thus
reducing the runtime by many-core parallel computation will be beneficial. Spatial …