1.
M.M.M. Al-Husari, B. Hendel, I.M. Jaimoukha, E.M. Kasenally, D.J.N. Limebeer, and A.Portone. Vertical stabilisation of Tokamak Plasmas. In Proceedings of the 30th Conference on Decision and Control, December 1992.
2.
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv, April 2018.
3.
D. Bertsekas. Reinforcement Learning and Optimal Control. Athena Scientific, 2019.
4.
Ilker Birbil and Shu-Chering Fang. An electromagnetism-like mechanism for global optimization. Journal of Global Optimization, 25:263–282, 03 2003.
5.
Léon Bottou, Frank E. Curtis, and Jorge Nocedal. Optimization methods for large-scale machine learning.
SIAM Review, 60:223–311, 2016.
MathSciNetCrossref6.
A. Bryson and Y. Ho. Applied Optimal Control. Hemisphere Publishing Company, 1975.
7.
Barbara Cannas, Gabriele Murgia, A Fanni, Piergiorgio Sonato, Augusto Montisci, and M.K. Zedda. Dynamic Neural Networks for Prediction of Disruptions in Tokamaks. CEUR Workshop Proceedings, 284, 01 2007.
8.
Wroblewski D. and et al. Tokamak disruption alarm based on neural network model of high-beta limit. Nuclear Fusion, 37(725), 11 1997.
9.
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever. Jukebox: A generative model for music, 2020.
10.
Dheeru Dua and Casey Graff. UCI machine learning repository, 2017.
11.
S. Dunbar. Stochastic Processes and Advanced Mathematical Finance Brief History of Mathematical Finance Rating Everyone. In Semantic Scholar, 2015.
12.
Pablo Ramon Escobal. Methods of Orbit Determination. Krieger Publishing Company, 1965.
13.
David Foster. Generative Deep Learning. O’Reilly Media, Inc., June 2019.
14.
David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1988.
15.
S. Haykin.
Neural Networks. Prentice-Hall, 1999.
zbMATH16.
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, and Douglas Eck. MUSIC TRANSFORMER:GENERATING MUSIC WITH LONG-TERM STRUCTURE. arKiv, 2019.
17.
Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew. Extreme learning machine: Theory and applications. Neurocomputing, 70(1):489–501, 2006. Neural Networks.
18.
P. Jackson. Introduction to Expert Systems, Third Edition. Addison-Wesley, 1999.
19.
Julian Kates-Harbeck, Alexey Svyatkovskiy, and William Tang. Predicting disruptive instabilities in controlled fusion plasmas through deep learning.
Nature, 568:526–531, April 2019.
Crossref20.
Diederik P. Kingma and Jimmy Lei Ba. ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION. In ICLR 2015, 2015.
21.
Daniel S. Kolosa. A Reinforcement Learning Approach to Spacecraft Trajectory Optimization. Technical Report Dissertations 3542, Western Michigan University, 2019.
22.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6), 2017.
23.
Y. Liang and JET EFDA Contributors. Overview of Edge Localized Modes Control in Tokamak Plasma. Technical Report Preprint of Paper for Fusion Science and Technology, JET-EFDA, 2017.
24.
Alan J Lockett and Risto Miikkulainen. Temporal Convolution Machines for Sequence Learning. Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin, 2009.
25.
Lopez. RNN, LSTM & GRU. dProgrammer Lopez, April 2019.
26.
Jere Schenck Meserole. Detection Filters for Fault-Tolerant Control of Turbofan Engines. Phd, Massachusetts Institute of Technology, 1981.
27.
28.
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw audio, 2016. cite arxiv:1609.03499.
29.
M. Paluszek, Y. Razin, G. Pajer, J. Mueller, and S.Thomas. Spacecraft Attitude and Orbit Control: Third Edition. Princeton Satellite Systems, 2019.
30.
Michael Phi. Illustrated Guide to LSTM’s and GRU’s: A step by step explanation. Towards Data Science, September 2018.
31.
G.A. Ratta, J..Vega, A. Murari, the EUROfusion MSTTeam, and JET Contributors. AUG-JET cross-tokamak disruption predictor. In 2nd IAEA TM, 2017.
32.
L.M. Rasdi Rere, Mohamad Ivan Fanany, and Aniati MurniA rymurthy. Simulated annealing algorithm for deep learning. Procedia Computer Science, 72:137–144, 2015.
33.
Joseph Rocca. Understanding Variational Autoencoders (VAEs). Towards Data Science, September 2019.
34.
Elizabeth Rosenthal. Artificial Intelligence Approach Points to Bright Future for Fusion Energy. Oak Ridge National Laboratory, 2019.
35.
S. Russell and P. Norvig.
Artificial Intelligence A Modern Approach Third Edition. Prentice-Hall, 2010.
zbMATH36.
Paul A. Samuelson. Mathematics of speculative price.
SIAM Review, 15(1):1–42, 1973.
MathSciNetCrossref37.
R.O. Sayer, Y.K.M. Peng, J.C. Wesley, S.C. Jardin, CA General Atomics, San Diego, and NJ Princeton Univ. ITER disruption modeling using TSC (Tokamak Simulation Code). Technical report, Oak Ridge National Laboratory, 11 1989.
38.
Luigi. Scibile. Non-linear control of the plasma vertical position in a tokamak. PhD thesis, University of Oxford, 1997.
39.
Richard Socher. Recursive Deep Learning for Natural Language Processing and Computer Vision. PhD thesis, Stanford University, August 2014.
40.
Russell Stewart. Maximum likelihood decoding with rnns - the good, the bad, and the ugly, 2016.
41.
Stephanie Thomas and Michael Paluszek.
MATLAB Machine Learning. Apress, 2017.
zbMATH42.
Stephanie Thomas and Michael Paluszek.
MATLAB Machine Learning Recipes: A Problem-Solution Approach. Apress, 2019.
zbMATH43.
P. Toiviainen and T. Eerola. MIDI toolbox 1.1.
https://github.com/miditoolbox/, 2016.
44.
Phillip Wang, 2019.
45.
W. E. Wiesel. Spaceflight Dynamics. McGraw-Hill, 1988.
46.
Geoffrey Zweig and Chris J.C. Burges. The microsoft research sentence completion challenge. Technical Report MSR-TR-2011-129, Microsoft, December 2011.