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 …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

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

Partial differential equations (PDEs) play a fundamental role in modeling and simulating
problems across a wide range of disciplines. Recent advances in deep learning have shown …

This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …

Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …

Tool wear prediction is of significance to improve the safety and reliability of machining tools,
given their widespread applications in nearly every branch of manufacturing. Mathematical …

The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and
weather prediction, hinge on the choice of turbulence models. The abundance of data from …

To overcome the limitations associated with purely physics-based and data-driven modeling
methods, hybrid, physics-based data-driven models have been developed, with improved …

We revisit the grid-point requirement estimates in Choi and Moin [“Grid-point requirements
for large eddy simulation: Chapman's estimates revisited,” Phys. Fluids 24, 011702 (2012)] …