There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

This chapter introduces a framework for combining scientific knowledge of physics-based
models with neural networks to advance scientific discovery. It explains termed physics …

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to
overcome common limitations shared by conventional reduced order models (ROMs)–built …

A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …

In this investigation, a data-driven turbulence closure framework is introduced and deployed
for the subgrid modelling of Kraichnan turbulence. The novelty of the proposed method lies …

Conventional reduced order modeling techniques such as the reduced basis (RB) method
(relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …

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

This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …