Predicting disruptive instabilities in controlled fusion plasmas through deep learning

By J. Kates-Harbeck, Alexey Svyatkovskiy, William M. Tang. Nature, 2019

325 citations recorded by the source corpus.

Subfields: Nuclear and High Energy Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/19d45de1f7891a1c8687c7e4eac222aca3330bb5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Nuclear and High Energy Physics

1. Jean-Marc Moret
2. Olivier Sauter
3. B. Duval

In Signal Processing

1. Yoshua Bengio
2. Koray Kavukcuoglu
3. Pushmeet Kohli
4. John C. Platt
5. Andrew Slavin Ross
6. Natasha Jaques
7. Konrad Paul Kording
8. Demis Hassabis

Citations in the corpus

• Magnetic control of tokamak plasmas through deep reinforcement learning (2022, background): “…(AI) has recently been identified as a “Priority Research Opportunity” for fusion control [14], building on demonstrated successes in reconstructing plasma shape parameters [15, 16], accelerating simulations using surrogate models [17, 18], and detecting impending plasma disruptions [19].”
• Tackling Climate Change with Machine Learning (2019, methodology): “While many of these methods are tuned to work on individual reactors, recent work has shown that deep learning may enable insights that generalize to multiple reactors [123].”