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Roadmap on metamaterial theory, modelling and design

  • Bryn Davies
  • , Stefan Szyniszewski
  • , Marcelo A. Dias
  • , Leo de Waal
  • , Anastasia Kisil
  • , Valery P Smyshlyaev
  • , Shane Cooper
  • , Ilia V. Kamotski
  • , Marie Touboul
  • , Richard V. Craster
  • , James R. Capers
  • , Simon A.R. Horsley
  • , Robert W. Hewson
  • , Matthew Santer
  • , Ryan Murphy
  • , Dilaksan Thillaithevan
  • , Simon J. Berry
  • , Gareth J. Conduit
  • , Jacob Earnshaw
  • , Nicholas Syrotiuk
  • Oliver Duncan, Łukasz Kaczmarczyk, Fabrizio Scarpa, John B. Pendry, Marc Martí-Sabaté, Sébastien Guenneau, Daniel Torrent, Elena Cherkaev, Niklas Wellander, Andrea Alù, Katie H. Madine, Daniel J. Colquitt, Ping Sheng, Luke G. Bennetts, Anastasiia O. Krushynska, Zhaohang Zhang, Mohammad J. Mirzaali, Amir Zadpoor
  • University of Warwick
  • Imperial College London
  • Durham University
  • University of Edinburgh
  • University of Manchester
  • University College London
  • University of Exeter
  • QinetiQ
  • University of Cambridge
  • The Studio
  • Manchester Metropolitan University
  • University of Glasgow
  • University of Bristol
  • University Jaume I
  • University of Utah, College Of Science
  • Luleå University of Technology
  • York College/The City University of New York
  • The Graduate Center
  • University of Liverpool
  • The Hong Kong University of Science and Technology
  • School of Mathematics and Statistics
  • ICS/University of Groningen
  • Delft University of Technology (TU Delft)

Résultats de recherche: Contribution à un journalArticle de révisionRevue par des pairs

Résumé

This Roadmap surveys the diversity of different approaches for characterising, modelling and designing metamaterials. It contains articles covering the wide range of physical settings in which metamaterials have been realised, from acoustics and electromagnetics to water waves and mechanical systems. In doing so, we highlight synergies between the many different physical domains and identify commonality between the main challenges. The articles also survey a variety of different strategies and philosophies, from analytic methods such as classical homogenisation to numerical optimisation and data-driven approaches. We highlight how the challenging and many-degree-of-freedom nature of metamaterial design problems call for techniques to be used in partnership, such that physical modelling and intuition can be combined with the computational might of modern optimisation and machine learning to facilitate future breakthroughs in the field.

langue originaleAnglais
Numéro d'article203002
journalJournal of Physics D: Applied Physics
Volume58
Numéro de publication20
Les DOIs
étatPublié - 19 mai 2025

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