Machine-learning inference of fluid variables from data using reservoir computing
This work addresses fluid dynamics prediction for researchers, but it is incremental as it applies existing reservoir computing methods to a new domain.
The authors tackled the problem of inferring microscopic and macroscopic behaviors of a 3D chaotic fluid flow using reservoir computing, achieving long-time motion inference of microscopic variables and reproducing energy spectra from past data.
We infer both microscopic and macroscopic behaviors of a three-dimensional chaotic fluid flow using reservoir computing. In our procedure of the inference, we assume no prior knowledge of a physical process of a fluid flow except that its behavior is complex but deterministic. We present two ways of inference of the complex behavior; the first called partial-inference requires continued knowledge of partial time-series data during the inference as well as past time-series data, while the second called full-inference requires only past time-series data as training data. For the first case, we are able to infer long-time motion of microscopic fluid variables. For the second case, we show that the reservoir dynamics constructed from only past data of energy functions can infer the future behavior of energy functions and reproduce the energy spectrum. It is also shown that we can infer a time-series data from only one measurement by using the delay coordinates. These implies that the obtained two reservoir systems constructed without the knowledge of microscopic data are equivalent to the dynamical systems describing macroscopic behavior of energy functions.