EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D
We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architectu...
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Veröffentlicht in: | Physics of plasmas 2024-09, Vol.31 (9) |
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creator | Madireddy, S. Akçay, C. Kruger, S. E. Amara, T. Bechtel Sun, X. McClenaghan, J. Koo, J. Samaddar, A. Liu, Y. Balaprakash, P. Lao, L. L. |
description | We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density
Jtor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed. |
doi_str_mv | 10.1063/5.0213609 |
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Jtor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.</description><identifier>ISSN: 1070-664X</identifier><identifier>EISSN: 1089-7674</identifier><identifier>DOI: 10.1063/5.0213609</identifier><identifier>CODEN: PHPAEN</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms and data structure ; Artificial neural networks ; Bayesian statistics ; Constraints ; Equilibrium ; Fusion energy ; Machine learning ; Magnetohydrodynamics ; Neural networks ; Nuclear fusion ; Plasma control ; Poloidal flux ; Predictive control ; Probability theory ; Real time ; Reduced order models ; Regression analysis ; Smoothness ; Statistical analysis ; Tokamaks ; Uncertainty</subject><ispartof>Physics of plasmas, 2024-09, Vol.31 (9)</ispartof><rights>Author(s)</rights><rights>2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c209t-5dc463557c438e0c39a2bd8b24071939bf074f60899abf70110ebaa8e123d34b3</cites><orcidid>0000-0003-1937-2675 ; 0000-0002-2635-7118 ; 0000-0003-3742-1485 ; 0009-0001-8102-3962 ; 0000-0001-8817-4643 ; 0000-0001-9174-6227 ; 0000-0002-0292-5715 ; 0000-0003-4735-0991 ; 0000-0001-9520-8012 ; 0000-0002-8192-8411 ; 0000-0002-0437-8655 ; 0000000204378655 ; 0000000191746227 ; 0000000202925715 ; 0000000281928411 ; 0000000226357118 ; 0000000347350991 ; 0000000337421485 ; 0000000188174643 ; 0000000195208012 ; 0000000319372675 ; 0009000181023962</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,782,786,887,27933,27934</link.rule.ids><backlink>$$Uhttps://www.osti.gov/servlets/purl/2477694$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Madireddy, S.</creatorcontrib><creatorcontrib>Akçay, C.</creatorcontrib><creatorcontrib>Kruger, S. E.</creatorcontrib><creatorcontrib>Amara, T. Bechtel</creatorcontrib><creatorcontrib>Sun, X.</creatorcontrib><creatorcontrib>McClenaghan, J.</creatorcontrib><creatorcontrib>Koo, J.</creatorcontrib><creatorcontrib>Samaddar, A.</creatorcontrib><creatorcontrib>Liu, Y.</creatorcontrib><creatorcontrib>Balaprakash, P.</creatorcontrib><creatorcontrib>Lao, L. L.</creatorcontrib><creatorcontrib>General Atomics, San Diego, CA (United States)</creatorcontrib><title>EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D</title><title>Physics of plasmas</title><description>We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density
Jtor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.</description><subject>Algorithms and data structure</subject><subject>Artificial neural networks</subject><subject>Bayesian statistics</subject><subject>Constraints</subject><subject>Equilibrium</subject><subject>Fusion energy</subject><subject>Machine learning</subject><subject>Magnetohydrodynamics</subject><subject>Neural networks</subject><subject>Nuclear fusion</subject><subject>Plasma control</subject><subject>Poloidal flux</subject><subject>Predictive control</subject><subject>Probability theory</subject><subject>Real time</subject><subject>Reduced order models</subject><subject>Regression analysis</subject><subject>Smoothness</subject><subject>Statistical analysis</subject><subject>Tokamaks</subject><subject>Uncertainty</subject><issn>1070-664X</issn><issn>1089-7674</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kUtLxTAQhYso-Fz4D4KuFKKTJk0ad-KzIOhCwV3Iqxi9t7kmLeLC_24ude1qZvHNmTlzquqQwBkBTs-bM6gJ5SA3qh0CrcSCC7a57gVgztnrdrWb8zsAMN60O9XPzW33jJ9SWPoL9JSi0SYsQh6DRXpwaPX2nYPN2MYhj0mHwTuUvJusdzgm5xMa_JT0opTxK6YPtIzOL1AfE_KfU1EyKUzLMjILTHYMcUBhQNdd1-Hr_Wqr14vsD_7qXvVye_N8dY8fHu-6q8sHbGuQI26cZZw2jbCMth4slbo2rjU1A0EklaYHwXpe7EptegGEgDdat57U1FFm6F51NOvG4kxlG0Zv38pJg7ejqpkQXLICHc_QKsXPyedRvccpDeUuRQkQymQr2kKdzJRNMefke7Uqz9PpWxFQ6whUo_4iKOzpzK436rX1f-BftuuGJQ</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Madireddy, S.</creator><creator>Akçay, C.</creator><creator>Kruger, S. 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E.</au><au>Amara, T. Bechtel</au><au>Sun, X.</au><au>McClenaghan, J.</au><au>Koo, J.</au><au>Samaddar, A.</au><au>Liu, Y.</au><au>Balaprakash, P.</au><au>Lao, L. L.</au><aucorp>General Atomics, San Diego, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D</atitle><jtitle>Physics of plasmas</jtitle><date>2024-09-01</date><risdate>2024</risdate><volume>31</volume><issue>9</issue><issn>1070-664X</issn><eissn>1089-7674</eissn><coden>PHPAEN</coden><abstract>We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. 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Jtor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0213609</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0003-1937-2675</orcidid><orcidid>https://orcid.org/0000-0002-2635-7118</orcidid><orcidid>https://orcid.org/0000-0003-3742-1485</orcidid><orcidid>https://orcid.org/0009-0001-8102-3962</orcidid><orcidid>https://orcid.org/0000-0001-8817-4643</orcidid><orcidid>https://orcid.org/0000-0001-9174-6227</orcidid><orcidid>https://orcid.org/0000-0002-0292-5715</orcidid><orcidid>https://orcid.org/0000-0003-4735-0991</orcidid><orcidid>https://orcid.org/0000-0001-9520-8012</orcidid><orcidid>https://orcid.org/0000-0002-8192-8411</orcidid><orcidid>https://orcid.org/0000-0002-0437-8655</orcidid><orcidid>https://orcid.org/0000000204378655</orcidid><orcidid>https://orcid.org/0000000191746227</orcidid><orcidid>https://orcid.org/0000000202925715</orcidid><orcidid>https://orcid.org/0000000281928411</orcidid><orcidid>https://orcid.org/0000000226357118</orcidid><orcidid>https://orcid.org/0000000347350991</orcidid><orcidid>https://orcid.org/0000000337421485</orcidid><orcidid>https://orcid.org/0000000188174643</orcidid><orcidid>https://orcid.org/0000000195208012</orcidid><orcidid>https://orcid.org/0000000319372675</orcidid><orcidid>https://orcid.org/0009000181023962</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms and data structure Artificial neural networks Bayesian statistics Constraints Equilibrium Fusion energy Machine learning Magnetohydrodynamics Neural networks Nuclear fusion Plasma control Poloidal flux Predictive control Probability theory Real time Reduced order models Regression analysis Smoothness Statistical analysis Tokamaks Uncertainty |
title | EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D |
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