An Optimized Dynamic Mode Decomposition Model Robust to Multiplicative Noise

Dynamic mode decomposition (DMD) is an efficient tool for decomposing spatio-temporal data into a set of low-dimensional modes, yielding the oscillation frequencies and growth rates physically significant modes. In this paper, we propose novel DMD model that can be used dynamical systems affected by multiplicative noise. We first derive maximum posteriori (MAP) estimator data-based linear system corrupted certain Applying penalty relaxation to MAP estimator, obtain proposed whose epigraphical limits are conventional optimized model. also alternating gradient descent method solving analyze its convergence behavior. The demonstrated on both synthetic numerically generated one-dimensional combustor shown have superior reconstruction properties compared state-of-the-art models. Considering noise ubiquitous in numerous systems, opens up new possibilities accurate modal decomposition.