A survey of published research, from the Essentia Memoria newsroom. It lists real papers, each looked up at its DOI registry; the game has not repeated their results.
Research survey
What AI research has found: energy and fusion
Happened: · Published:
7 published works on energy and fusion, each looked up at its DOI registry before it was listed here, with what each one reports in plain words.
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News from the Essentia Memoria game universe. What AI research has found: energy and fusion. 7 published works on energy and fusion, each looked up at its DOI registry before it was listed here, with what each one reports in plain words. This survey lists what each paper reports. The game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited. This report was written from the world record by a fixed template. The full story and its source records are on essentiamemoria.com.
The question
Can a learning controller hold a fusion plasma in place?
Claims, and where each one comes from
- Where this research lives in the game (a place the world grammar builds)power-plant → factory
Game simulation · Set in code
src/lib/world-grammar/place-grammar.ts · resolvePurpose('power-plant') - Published works in this survey, each resolved at its DOI registry7
Real-world knowledge · Source [1] [2] [3] [4] [5] [6] [7]
- A controller trained by reinforcement learning shaped and held the plasma of a real tokamak with its magnetic coils.Degrave 2022
Real-world knowledge · Source [1]
- Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart GridsZheng 2017
Real-world knowledge · Source [2]
- A review on artificial intelligence based load demand forecasting techniques for smart grid and buildingsRaza 2015
Real-world knowledge · Source [3]
- Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent MechanismHe 2017
Real-world knowledge · Source [4]
- Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigmAhmad 2022
Real-world knowledge · Source [5]
- Machine Learning Methods for Attack Detection in the Smart GridÖzay 2015
Real-world knowledge · Source [6]
- Opportunities and Challenges of Wireless Sensor Networks in Smart GridGüngör 2010
Real-world knowledge · Source [7]
References, checked at their registry
[1] Degrave (2022). Magnetic control of tokamak plasmas through deep reinforcement learning. Nature. doi:10.1038/s41586-021-04301-9
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “Magnetic control of tokamak plasmas through deep reinforcement learning” (2022)
[2] Zheng (2017). Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids. IEEE Transactions on Industrial Informatics. doi:10.1109/tii.2017.2785963
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids” (2018)
[3] Raza (2015). A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews. doi:10.1016/j.rser.2015.04.065
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings” (2015)
[4] He (2017). Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism. IEEE Transactions on Smart Grid. doi:10.1109/tsg.2017.2703842
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism” (2017)
[5] Ahmad (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews. doi:10.1016/j.rser.2022.112128
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm” (2022)
[6] Özay (2015). Machine Learning Methods for Attack Detection in the Smart Grid. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/tnnls.2015.2404803
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “Machine Learning Methods for Attack Detection in the Smart Grid” (2016)
[7] Güngör (2010). Opportunities and Challenges of Wireless Sensor Networks in Smart Grid. IEEE Transactions on Industrial Electronics. doi:10.1109/tie.2009.2039455
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:55 PM UTC · the registry holds “Opportunities and Challenges of Wireless Sensor Networks in Smart Grid” (2010)
Review
Not peer reviewed. No human scientist has reviewed this survey. Its template and landmark summaries were written with an AI assistant (Claude); the papers it lists are real and each was checked at its registry, so anyone can follow the links and read them.
- Each listed DOI was resolved, and its title, year and first author matched the listing.
- Each work the search found is held by Crossref, a registry independent of the search.
- Each number in the headline and summary is accounted for by a listed claim.
- Not shown: 2 candidate work(s) whose registry record did not confirm them (10.17775/cseejpes.2018.00520 mismatch; 10.1109/jsyst.2014.2341597 mismatch).
Code version: fa127b6f988c
What the record does not say
This survey lists what each paper reports; the game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited.
How this was written
Landmark works named by the desk, then the most-cited works with a DOI that OpenAlex returns when titles and abstracts are searched for (tokamak OR "nuclear fusion" OR "fusion plasma" OR "plasma control" OR "power grid" OR "smart grid") AND ("machine learning" OR "deep learning" OR "neural network" OR "neural networks" OR "artificial intelligence" OR convolutional OR "reinforcement learning" OR "language model" OR transformer); a hit is kept only if its own title names the field and its title or abstract names an AI method, as whole words, and the hits that name the most of these words in their title come first. Each candidate was looked up at Crossref (OpenAlex as second resolver) and its title, year and first author compared; a work the search found is shown only when Crossref holds it. Landmark summaries were written by the desk; the other works are listed by their own titles.
Source records: code:src/lib/world-grammar/place-grammar.ts#resolvePurpose · doi:10.1038/s41586-021-04301-9 · doi:10.1109/tii.2017.2785963 · doi:10.1016/j.rser.2015.04.065 · doi:10.1109/tsg.2017.2703842 · doi:10.1016/j.rser.2022.112128 · doi:10.1109/tnnls.2015.2404803 · doi:10.1109/tie.2009.2039455