A physics-informed deep operator network for modeling of atmospheric RF plasmas

Wenkai Li, Kun Sun, Yuantao Zhang

Open source

DOI: 10.1088/2632-2153/ae82b5
Published: 2026-07-08
Container: Machine Learning: Science and Technology
Publisher: IOP Publishing
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1088/2632-2153/ae82b5,
  title = {A physics-informed deep operator network for modeling of atmospheric RF plasmas},
  author = {Wenkai Li and Kun Sun and Yuantao Zhang},
  year = {2026},
  journal = {Machine Learning: Science and Technology},
  doi = {10.1088/2632-2153/ae82b5},
  url = {https://doi.org/10.1088/2632-2153/ae82b5}
}

RIS

TY  - JOUR
TI  - A physics-informed deep operator network for modeling of atmospheric RF plasmas
AU  - Wenkai Li
AU  - Kun Sun
AU  - Yuantao Zhang
PY  - 2026
JO  - Machine Learning: Science and Technology
DO  - 10.1088/2632-2153/ae82b5
UR  - https://doi.org/10.1088/2632-2153/ae82b5
ER  -

APA

Li, W., Sun, K., & Zhang, Y. (2026). A physics-informed deep operator network for modeling of atmospheric RF plasmas. Machine Learning: Science and Technology. https://doi.org/10.1088/2632-2153/ae82b5

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crossref · retrieved 2026-11-10T12:03:33.863Z