Dynamic mode decomposition for underwater acoustics on autonomous platforms

Abstract

A sufficiently accurate representation of the ocean physics is needed for reliable underwater acoustics forecasting. Ocean physics are often simulated by high-dimensional data-assimilative numerical ocean simulations. Due to operational constraints such as computing, memory, communication-bandwidth, etc., it is infeasible to run these numerical simulations onboard the platforms. In this work, we employ the onboard data-assimilative incremental, Low-Rank Dynamic Mode Decomposition (iLRDMD) for forecast transmission, onboard computation, and data assimilation. The iLRDMD is an adaptive reduced-order model (ROM) that uses proper orthogonal decomposition (POD) and DMD for compression and transmission of high-fidelity ocean forecast to communication-disadvantaged platforms. It provides reduced-order onboard forecasting and also enables efficient updates of the DMD model itself from inputs from remote centers. Finally, when sparse observations are made by the platform, the iLRDM uses Bayesian data assimilation to correct both the forecasts and the DMD model. We demonstrate the use of deterministic and data-assimilative iLRDMD forecasted ocean fields for underwater acoustics computations in real ocean examples. We further explore the use of our adaptive ROM for joint reduction of the ocean physics and acoustics fields.