Physically Interpretable Representation and Controlled Generation for Turbulence Data
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.
Authors
- Tiffany Fan (ORCID: https://orcid.org/0000-0002-7598-9218)
- Alireza Doostan (ORCID: https://orcid.org/0000-0002-3020-3891)
- Marta D’Elia (ORCID: https://orcid.org/0000-0002-6536-6295)
- Murray Cutforth
- Eric Darve (ORCID: https://orcid.org/0000-0002-1938-3836)
- Alexandre Cortiella (ORCID: https://orcid.org/0000-0003-1387-2126)
Institutions
- National Laboratory of the Rockies (US)
- University of Colorado Boulder (US)
- University of Colorado System (US)
- Stanford University (US)
Publication Details
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1109/cai64502.2025.00188
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00