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AI-Based Prediction and Control of Tokamaks : = Combining Simulations and Experimental Data.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	AI-Based Prediction and Control of Tokamaks :/
Reminder of title:	Combining Simulations and Experimental Data.
Author:	Abbate, Joseph Albert.
Description:	1 online resource (138 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Plasma physics. -
Online resource:	click for full text (PQDT)
ISBN:	9798382806853

AI-Based Prediction and Control of Tokamaks : = Combining Simulations and Experimental Data.
Abbate, Joseph Albert.

AI-Based Prediction and Control of Tokamaks :Combining Simulations and Experimental Data. - 1 online resource (138 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Princeton University, 2024.

Includes bibliographical references

A unified AI (artificial intelligence) approach to predict and control the dynamics of kinetic plasma profiles in fusion reactors is presented. On one hand, it is demonstrated that empirical models trained on experimental data ("data-driven models") significantly outperform the state-of-the-art ASTRA and TRANSP codes ("simulations") when predicting within the distribution of the training set. On the other hand, it is demonstrated that simulations can perform as well or better than data-driven models when extrapolating outside of the training distribution. Multiple AI-based methodologies for combining the data-driven models and simulations, leveraging data from multiple machines (DIII-D and AUG), are presented. One of the methodologies better extrapolates to new regimes than either data-driven models or simulations alone. Applications of the holistic approach to the task of commissioning a new reactor such as ITER are discussed. A successful model-predictive control test at DIII-D based on the methodology is described.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382806853Subjects--Topical Terms:

1030958
Plasma physics.
Subjects--Index Terms:

Data-driven modelsIndex Terms--Genre/Form:

554714
Electronic books.

AI-Based Prediction and Control of Tokamaks : = Combining Simulations and Experimental Data.
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502 $a Thesis (Ph.D.)--Princeton University, 2024.
504 $a Includes bibliographical references
520 $a A unified AI (artificial intelligence) approach to predict and control the dynamics of kinetic plasma profiles in fusion reactors is presented. On one hand, it is demonstrated that empirical models trained on experimental data ("data-driven models") significantly outperform the state-of-the-art ASTRA and TRANSP codes ("simulations") when predicting within the distribution of the training set. On the other hand, it is demonstrated that simulations can perform as well or better than data-driven models when extrapolating outside of the training distribution. Multiple AI-based methodologies for combining the data-driven models and simulations, leveraging data from multiple machines (DIII-D and AUG), are presented. One of the methodologies better extrapolates to new regimes than either data-driven models or simulations alone. Applications of the holistic approach to the task of commissioning a new reactor such as ITER are discussed. A successful model-predictive control test at DIII-D based on the methodology is described.
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