Physics-augmented machine learning techniques for modeling tokamak dynamics

Abbate, J. A.; Fable, E.; Pankin, A.; Tardini, G.; Kolemen, E.

Local TagsRelease HistoryDetailsSummary

Physics-augmented machine learning techniques for modeling tokamak dynamics

Abbate, J. A., Fable, E., Pankin, A., Tardini, G., & Kolemen, E. (2023). Physics-augmented machine learning techniques for modeling tokamak dynamics. Talk presented at 65th Annual Meeting of the APS Division of Plasma Physics. Denver, CO. 2023-10-30 - 2023-11-03.

Item is Released

show all hide all

Basic

show hide

Item Permalink: https://hdl.handle.net/21.11116/0000-000E-A9CC-E Version Permalink: https://hdl.handle.net/21.11116/0000-000E-A9CD-D

Genre: Talk

Files

show Files

Locators

show

Creators

show

hide

Creators:
Abbate, J. A.¹, Author
Fable, E.², Author
Pankin, A.¹, Author
Tardini, G.², Author
Kolemen, E.¹, Author

Affiliations:
1External Organizations, ou_persistent22
2Tokamak Scenario Development (E1), Max Planck Institute for Plasma Physics, Max Planck Society, ou_1856321

Content

show

Details

show

hide

Language(s): eng - English

Dates: Created: 2023

Publication Status: Not specified

Pages: -

Publishing info: -

Table of Contents: -

Rev. Type: -

Identifiers: -

Degree: -

Event

show

hide

Title: 65th Annual Meeting of the APS Division of Plasma Physics

Place of Event: Denver, CO

Start-/End Date: 2023-10-30 - 2023-11-03

Legal Case

show

Project information

show

Source

show