Work

Physically Interpretable Representation and Controlled Generation for Turbulence Data

Year: 2025

Type: conference-paper

Abstract: Computational Fluid Dynamics (CFD) is central to fluid mechanics, offering precise simulations of fluid behavior through partial differential equations (PDEs). Traditional CFD methods, such as those based on finite difference and finite volume schemes, are resource-consuming, especially for high-fidelity simulations of complex flows. Understanding such datasets presents unique challenges due to their high dimensionality, inherent stochasticity, and limited data availability.

Source: IEEE Conference on Artificial Intelligence (CAI)

DOI: 10.1109/cai64502.2025.00188

Authors: Tiffany Fan, Murray Cutforth, Marta D'Elia, Alexandre Cortiella, Alireza Doostan, Eric Darve

Institutions: Stanford University, National Laboratory of the Rockies, University of Colorado Boulder

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4412082750

FWCI: 2.291

Cites: 2

Cited by: 1

Related to: 10