Metamaterial×Machine learning

The Frontier Brief for this collision is still being written. The evidence below already reflects the current model run.

Pre-company space. No named players have staked this collision yet — an emerging pairing where the field, not a firm, is the story so far.

7 researchers publish on both sides of this collision without the fields themselves having met. Every name below is counted from papers in the corpus — not inferred.

• Shulin SunSuzhou University of Science and Technology
  5/1
• Yongmin LiuNational University of Defense Technology
  5/1
• Feng ChengNortheastern University
  3/1
• Wei MaNortheastern University
  2/1
• Qinlong WenNortheastern University
  1/1
• Yihao XuNortheastern University
  1/1
• Jingchao JiangUniversity of Auckland
  1/1

• Nanyang Technological UniversitySG
  94/89
• Stanford UniversityUS
  52/131
• Chinese Academy of SciencesCN
  71/72
• National University of SingaporeSG
  77/54
• University of California, BerkeleyUS
  31/132
• Zhejiang UniversityCN
  84/44

• Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy2019 · 617 citations · DOI ↗
  1
• Generalized bulk–boundary correspondence in non-Hermitian topolectrical circuits2020 · 876 citations · DOI ↗
  2
• Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning2023 · 359 citations · DOI ↗
  2
• Strongly Enhanced Sensitivity in Planar Microwave Sensors Based on Metamaterial Coupling2018 · 339 citations · DOI ↗
  2

Counted from the corpus. Institution counts use best-effort affiliation (every author on a paper is paired with every institution on it), so read them as presence, not headcount.