mlsanityData to 14 Oct 2026

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks

arXiv 2404.12416 ↗First posted 18 Apr 2024physics.plasm-ph cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics of the plasma and controlling it. As such, it is crucial that high quality models are developed to assist in overcoming these obstacles. In this work, we take an entirely data driven approach to learn such a model. In particular, we use historical data from the DIII-D tokamak to train a deep recurrent network that is able to predict the full time evolution of plasma discharges (or "shots"). Following this, we investigate how different training and inference procedures affect the quality and calibration of the shot predictions.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 18 Apr 2024.

Builds on

12 of 33 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  4. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  5. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 422
  6. 2017Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equationscited by 208
  7. 2020Augmenting Physical Models with Deep Networks for Complex Dynamics Forecastingcited by 95
  8. 2019Nonlinear System Identification: A User-Oriented Roadmapcited by 57
  9. 2021Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantificationcited by 57
  10. 2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 44
  11. 2020Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Predictioncited by 18
  12. 2023Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactorscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 6.

9 papers.

  1. 2024Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learningcited by 11
  2. 2025Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCVcited by 4
  3. 2026TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Modelscited by 3
  4. 2025Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learningcited by 2
  5. 2026Offline Reinforcement Learning for Rotation Profile Control in Tokamakscited by 2
  6. 2026Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learningcited by 1
  7. 2026Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMarkcited by 0
  8. 2026Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models: A Nuclear Fusion Case Studycited by 0
  9. 2026Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networkscited by 0