Diverse performance in compressive sensing-based reconstruction of an oscillatory dynamical system

Even though compressive sensing (CS) has been successfully implemented in numerous chaotic dynamical systems, there remains a lack of in-depth understanding of how this inverse problem method performs when reconstructing periodic dynamical systems. In this work, we investigate the reconstruction of a periodic dynamical system based on CS, considering the Landau–Stuart (LS) oscillator as a paradigmatic model. Unlike chaotic systems, the periodic dynamics bring some challenges to robust reconstruction by CS. We identified that by introducing slight differences or diversity in the dynamics of coupled Landau–Stuart (LS) oscillator systems, for example, coupling asymmetry, frequency detuning, and amplitude disparity, the process of achieving robust reconstruction by CS becomes considerably smooth.