Lim, C. W.

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Lim, C. W.

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  • Publication
    Vibration Resonance and Dynamic Characteristics of Pillared Phononic Crystals and Acoustic Metamaterials
    (Elsevier, 2023)
    Gulzari, Muhammad 
    ;
    Lim, C. W. 
    Engineered resonance phenomenon on surfaces have created an unprecedented world of surface science and technology. Branching resonant substructure as pillared phononic crystals and metamaterials emerge as a new class of synthetic structures with peculiar wave dispersion and dynamic properties that cannot be observed in natural materials. A fundamental property of interest includes dual behavior of pillared system that means exhibition of both Bragg and hybridization bandgaps. The rich resonance properties by this simple structure frontier a whole new research field in phononic crystals and metamaterials. The purpose of this article is to reproduce and combine different surface resonance phenomena reported in the literature on pillared structures with an insight on historical context, recent developments and future research prospects. The collection of findings reported here may provide a comprehensive insight on pillared resonances and help resolve the current challenges in the field to foster research spin-offs.
    Scopus© Citations 1  27
  • Publication
    Machine learning and deep learning in phononic crystals and metamaterials – A review
    (Elsevier, 2022-12)
    Gulzari, Muhammad 
    ;
    Kennedy, John 
    ;
    Lim, C. W. 
    Machine learning (ML), as a component of artificial intelligence, encourages structural design exploration which leads to new technological advancements. By developing and generating data-driven methodologies that supplement conventional physics and formula-based approaches, deep learning (DL), a subset of machine learning offers an efficient way to understand and harness artificial materials and structures. Recently, acoustic and mechanics communities have observed a surge of research interest in implementing machine learning and deep learning methods in the design and optimization of artificial materials. In this review we evaluate the recent developments and present a state-of-the-art literature survey in machine learning and deep learning based phononic crystals and metamaterial designs by giving historical context, discussing network architectures and working principles. We also explain the application of these network architectures adopted for design and optimization of artificial structures. Since this multidisciplinary research field is evolving, a summary of the future prospects is also covered. This review article serves to update the acoustics, mechanics, physics, material science and deep learning communities about the recent developments in this newly emerging research direction
      2Scopus© Citations 52