IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v187y2020i3d10.1007_s10957-019-01614-8.html
   My bibliography  Save this article

Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials

Author

Listed:
  • Andrea Bacigalupo

    (IMT School for Advanced Studies)

  • Giorgio Gnecco

    (IMT School for Advanced Studies)

  • Marco Lepidi

    (University of Genoa)

  • Luigi Gambarotta

    (University of Genoa)

Abstract

Recently, an increasing research effort has been dedicated to analyze the transmission and dispersion properties of periodic acoustic metamaterials, characterized by the presence of local resonators. Within this context, particular attention has been paid to the optimization of the amplitudes and center frequencies of selected stop and pass bands inside the Floquet–Bloch spectra of the acoustic metamaterials featured by a chiral or antichiral microstructure. Novel functional applications of such research are expected in the optimal parametric design of smart tunable mechanical filters and directional waveguides. The present paper deals with the maximization of the amplitude of low-frequency band gaps, by proposing suitable numerical techniques to solve the associated optimization problems. Specifically, the feasibility and effectiveness of Radial Basis Function networks and Quasi-Monte Carlo methods for the interpolation of the objective functions of such optimization problems are discussed, and their numerical application to a specific acoustic metamaterial with tetrachiral microstructure is presented. The discussion is motivated theoretically by the high computational effort often needed for an exact evaluation of the objective functions arising in band gap optimization problems, when iterative algorithms are used for their approximate solution. By replacing such functions with suitable surrogate objective functions constructed applying machine-learning techniques, well-performing suboptimal solutions can be obtained with a smaller computational effort. Numerical results demonstrate the effective potential of the proposed approach. Current directions of research involving the use of additional machine-learning techniques are also presented.

Suggested Citation

  • Andrea Bacigalupo & Giorgio Gnecco & Marco Lepidi & Luigi Gambarotta, 2020. "Machine-Learning Techniques for the Optimal Design of Acoustic Metamaterials," Journal of Optimization Theory and Applications, Springer, vol. 187(3), pages 630-653, December.
  • Handle: RePEc:spr:joptap:v:187:y:2020:i:3:d:10.1007_s10957-019-01614-8
    DOI: 10.1007/s10957-019-01614-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-019-01614-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10957-019-01614-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. R. Zoppoli & M. Sanguineti & T. Parisini, 2002. "Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 112(2), pages 403-440, February.
    2. Miguel Molerón & Chiara Daraio, 2015. "Acoustic metamaterial for subwavelength edge detection," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
    3. Juliane Müller & Christine Shoemaker, 2014. "Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems," Journal of Global Optimization, Springer, vol. 60(2), pages 123-144, October.
    4. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2013. "Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 380-416, February.
    5. G. Gnecco & M. Sanguineti, 2010. "Suboptimal Solutions to Dynamic Optimization Problems via Approximations of the Policy Functions," Journal of Optimization Theory and Applications, Springer, vol. 146(3), pages 764-794, September.
    6. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonio Pita & Francisco J. Rodriguez & Juan M. Navarro, 2021. "Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data," IJERPH, MDPI, vol. 18(16), pages 1-21, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2014. "Approximate dynamic programming for stochastic N-stage optimization with application to optimal consumption under uncertainty," Computational Optimization and Applications, Springer, vol. 58(1), pages 31-85, May.
    2. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2013. "Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 380-416, February.
    3. Giorgio Gnecco, 2016. "On the Curse of Dimensionality in the Ritz Method," Journal of Optimization Theory and Applications, Springer, vol. 168(2), pages 488-509, February.
    4. Juliane Müller, 2017. "SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems," INFORMS Journal on Computing, INFORMS, vol. 29(4), pages 581-596, November.
    5. Huster, Wolfgang R. & Schweidtmann, Artur M. & Mitsos, Alexander, 2020. "Globally optimal working fluid mixture composition for geothermal power cycles," Energy, Elsevier, vol. 212(C).
    6. Jianyuan Zhai & Fani Boukouvala, 2022. "Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization," Journal of Global Optimization, Springer, vol. 82(1), pages 21-50, January.
    7. M. Baglietto & C. Cervellera & M. Sanguineti & R. Zoppoli, 2010. "Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling," Computational Optimization and Applications, Springer, vol. 47(2), pages 349-376, October.
    8. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Global Optimization for Black-box Simulation via Sequential Intrinsic Kriging," Other publications TiSEM 8fa8d96f-a086-4c4b-88ab-9, Tilburg University, School of Economics and Management.
    9. Chong Li & Xinxin Liao & Zhi-Ke Peng & Guang Meng & Qingbo He, 2023. "Highly sensitive and broadband meta-mechanoreceptor via mechanical frequency-division multiplexing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Yurou Jia & Suying Zhang & Xuan Zhang & Houyou Long & Caibin Xu & Yechao Bai & Ying Cheng & Dajian Wu & Mingxi Deng & Cheng-Wei Qiu & Xiaojun Liu, 2024. "Compact meta-differentiator for achieving isotropically high-contrast ultrasonic imaging," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    11. Nicolau Andrés-Thió & Mario Andrés Muñoz & Kate Smith-Miles, 2022. "Bifidelity Surrogate Modelling: Showcasing the Need for New Test Instances," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3007-3022, November.
    12. Miriyala, Srinivas Soumitri & Subramanian, Venkat & Mitra, Kishalay, 2018. "TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study," European Journal of Operational Research, Elsevier, vol. 264(1), pages 294-309.
    13. Ze-Qi Lu & Long Zhao & Hai-Ling Fu & Eric Yeatman & Hu Ding & Li-Qun Chen, 2024. "Ocean wave energy harvesting with high energy density and self-powered monitoring system," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    14. Gandhi, Akhilesh & Zantye, Manali S. & Faruque Hasan, M.M., 2022. "Cryogenic energy storage: Standalone design, rigorous optimization and techno-economic analysis," Applied Energy, Elsevier, vol. 322(C).
    15. G. Gnecco & M. Sanguineti, 2010. "Estimates of Variation with Respect to a Set and Applications to Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 145(1), pages 53-75, April.
    16. Tsay, Calvin, 2024. "A Quantile Neural Network Framework for Twostage Stochastic Optimization," DES - Working Papers. Statistics and Econometrics. WS 43773, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Zan Yang & Haobo Qiu & Liang Gao & Chen Jiang & Jinhao Zhang, 2019. "Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems," Journal of Global Optimization, Springer, vol. 74(2), pages 327-359, June.
    18. Simpson, Michael C. & Chatzopoulou, Maria Anna & Oyewunmi, Oyeniyi A. & Le Brun, Niccolo & Sapin, Paul & Markides, Christos N., 2019. "Technoeconomic analysis of internal combustion engine – organic Rankine cycle systems for combined heat and power in energy-intensive buildings," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    19. Mehdad, E., 2015. "Kriging metamodels and global opimization in simulation," Other publications TiSEM 5b5c276a-fe68-4ce9-b8a8-1, Tilburg University, School of Economics and Management.
    20. Charles Audet & Michael Kokkolaras & Sébastien Le Digabel & Bastien Talgorn, 2018. "Order-based error for managing ensembles of surrogates in mesh adaptive direct search," Journal of Global Optimization, Springer, vol. 70(3), pages 645-675, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joptap:v:187:y:2020:i:3:d:10.1007_s10957-019-01614-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.