Troubleshooting experiments using machine learning

Machine learning has already proven to be a useful tool for both data analysis and synthesis of mathematical models of complicated fluid systems directly from experimental measurements. This capability can be leveraged effectively to help troubleshoot experiments. While some types of experimental issues are easy to catch, others are too subtle to discover without an extensive and laborious analysis and often go undetected. In this talk we illustrate how slow variation in global parameters (e.g, associated with fluid leaks or drift in temperature or driving current) can be detected in an experimental setup generating a complicated (weakly turbulent) fluid flow in a thin layer of electrolyte driven by the Lorentz force. Machine learning is used to identify a set of governing equations (continuity and momentum balance), with a set of coefficients which depend on global parameters. Consequently, variation in the coefficients can be used to unravel the drift in the global parameters. This approach is widely applicable and can be used to diagnose a variety of other problems in various experimental setups by analyzing the data in real time or ex post facto.