Session Materials

Data-driven Exploration for SF6 alternative Gas with Quantum Mechanics-assisted Machine Learning

Ref D1-11058-2024 • 2024
  • Price for member: 20 €
  • Price for non member: 30 €
Data-driven Exploration for SF6 alternative Gas with Quantum Mechanics-assisted Machine Learning

Summary

Session Delegates (Members and non-members): access these materials for free via your Session registration account.

Additional informations

Publication type Session Materials
Reference D1-11058-2024
Publication year
Publisher CIGRE
Study committees
  • Materials and emerging test techniques (D1)
File size 968 KB
Price for non member 30 €
Price for member 20 €

Authors

Masahiro SATO, Hajime SHIMAKAWA, Akiko KUMADA

Keywords

SF6 Alternative, Eco-friendly, Dielectric Breakdown Strength, Machine Learning, Quantum Mechanics

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Data-driven Exploration for SF6 alternative Gas with Quantum Mechanics-assisted Machine Learning
Data-driven Exploration for SF6 alternative Gas with Quantum Mechanics-assisted Machine Learning