Machine learning×Thermal conduction

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.

8 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.

• Kedar HippalgaonkarInstitute for Infocomm Research
  1/1
• Hongwei DuNational Institute of Clean and Low-Carbon Energy
  1/1
• Lanting ZhangInstitute for Advanced Study
  1/1
• Hong WangNational Institute of Clean and Low-Carbon Energy
  1/1
• Yuanxun ZhouSuzhou Research Institute
  1/1
• Jian HuiInstitute for Advanced Study
  1/1
• Xiang HuangInstitute for Advanced Study
  1/1
• Yuan DongHangzhou Dianzi University
  1/1

• Stanford UniversityUS
  131/7
• Tsinghua UniversityCN
  73/12
• Chinese Academy of SciencesCN
  72/10
• University of California, BerkeleyUS
  132/4
• Massachusetts Institute of TechnologyUS
  63/7
• Zhejiang UniversityCN
  44/9

• Multi-agent reinforcement learning as a rehearsal for decentralized planning2016 · 356 citations · DOI ↗
  2
• Federated Multiagent Actor–Critic Learning for Age Sensitive Mobile-Edge Computing2021 · 126 citations · DOI ↗
  2
• Blockchain-based Node-aware Dynamic Weighting Methods for Improving Federated Learning Performance2019 · 97 citations · DOI ↗
  2
• Two temperature fractional order thermoelasticity theory in a spherical domain2019 · 22 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.