Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas

Ali Mesbah, David B. Graves

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

Abstract

Machine learning (ML) is a set of computational tools that can analyze and utilize large amounts of data for many different purposes. Recent breakthroughs in ML and artificial intelligence largely enabled by advances in computing power and parallel computing present cross-disciplinary research opportunities to exploit some of these techniques in the field of non-equilibrium plasma (NEP) studies. This paper presents our perspectives on how ML can potentially transform modeling and simulation, real-time monitoring, and control of NEP.

Original languageEnglish (US)
Article number30LT02
JournalJournal of Physics D: Applied Physics
Volume52
Issue number30
DOIs
StatePublished - May 22 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Acoustics and Ultrasonics
  • Surfaces, Coatings and Films

Fingerprint

Dive into the research topics of 'Machine learning for modeling, diagnostics, and control of non-equilibrium plasmas'. Together they form a unique fingerprint.

Cite this