Data-driven detection of drifting system parameters
arXiv:2111.12114
Abstract
Data taken from observations of the natural world or laboratory measurements often depend on parameters which can vary in unexpected ways. In this paper we demonstrate how machine learning can be leveraged to detect changes in global parameters from variations in an identified model using only observational data. This capability, when paired with first principles analysis, can effectively distinguish the effects of these changing parameters from the intrinsic complexity of the system. Here we illustrate this by identifying a set of governing equations for an experiment generating a weakly turbulent fluid flow, then analyzing variation in the coefficients of these equations to unravel the drift in its physical parameters.
arXiv comment9 pages, 6 figures, as reported at the 74th Annual Meeting of the American Physical Society's Division of Fluid Dynamics
References in corpus (9)
- Machine learning accelerated computational fluid dynamics
- Low-dimensional dynamics embedded in a plane Poiseuille flow turbulence : Traveling-wave solution is a saddle point ?
- Using Noisy or Incomplete Data to Discover Models of Spatiotemporal Dynamics
- Forecasting Fluid Flows Using the Geometry of Turbulence
- Bifurcations in a Quasi-Two-Dimensional Kolmogorov-Like Flow
- Rapid estimation of drifting parameters in continuously measured quantum systems
- Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)
- Unstable Equilibria and Invariant Manifolds in Quasi-Two-Dimensional Kolmogorov-like Flow
- Heteroclinic and Homoclinic Connections in a Kolmogorov-Like Flow