J Kou, W Zhang - Progress in Aerospace Sciences, 2021 - Elsevier
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 …
S Fresca, A Manzoni - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
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 Daw, A Karpatne, WD Watkins… - Knowledge Guided …, 2022 - taylorfrancis.com
This chapter introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. It explains termed physics …
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 …
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 …
Conventional reduced order modeling techniques such as the reduced basis (RB) method (relying, eg, on proper orthogonal decomposition (POD)) may incur in severe limitations …
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 …