Learning fluid flow physics from noisy, incomplete, experimental data
Abstract
Purely data-driven methods have shown a lot of promise in identifying models of simple, low-dimensional systems from data which have a low level of noise and provide a complete description of the system state. However, they fall apart for data that is high-dimensional, noisy, or incomplete, which is common in fluid dynamics. We show that this challenge can be addressed by augmenting the data-driven approach with a few general physical constraints and using a weak formulation of the model. To illustrate this, we construct a quantitative two-dimensional model of a weakly turbulent flow in a thin layer of electrolyte driven by Lorentz force from PIV data on a coarse spatiotemporal grid. Our hybrid approach also allows reconstruction of the latent variables that cannot be measured directly, e.g., pressure and forcing field.
This material is based upon work supported by NSF under Grants No. CMMI-1725587 and CMMI-2028454.- Publication:
-
APS Division of Fluid Dynamics Meeting Abstracts
- Pub Date:
- 2020
- Bibcode:
- 2020APS..DFDK09018K