Local Flow Environment as Information Processing Medium

Recent advances in sensor technology have opened the door to applying distributed sensing to bodies in complex flow environments. However, the increased computational burden of processing the resulting information is not trivial. Using the mathematical framework of reservoir computing, we demonstrate that it is possible to offload some of this burden to the dynamics of the environment. The local flow environment may be treated as a non-linear operator which maps an input signal, a perturbation of the mean external flow, into a high dimensional latent space. A complex non-linear output function, such as a control signal, may then be computed by strategically sampling this space with a distributed sensor array and combining the readouts using a simple weighted sum. In this work, we demonstrate this concept using computational simulations of flow in an open-cavity and explore how sensor placement and cavity geometry affect the information processing capability. We find that placing an array of sensors at the bottom of the cavity gives better computational performance on a benchmark non-linear task than uniform sampling throughout the volume.