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

arXiv: https://arxiv.org/abs/2404.12416 · HTML: https://mlsanity.com/paper/2404.12416/

## 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 (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1708.00588/) (2017)
- [Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting](https://mlsanity.com/paper/2010.04456/) (2020)
- [Nonlinear System Identification: A User-Oriented Roadmap](https://mlsanity.com/paper/1902.00683/) (2019)
- [Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification](https://mlsanity.com/paper/2109.10254/) (2021)
- [Physics-Guided Deep Learning for Dynamical Systems: A Survey](https://mlsanity.com/paper/2107.01272/) (2021)
- [Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction](https://mlsanity.com/paper/2006.12682/) (2020)
- [Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors](https://mlsanity.com/paper/2310.20079/) (2023)

## Built on by (most cited first, 9 of 9)

- [Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2410.11234/) (2024)
- [Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV](https://mlsanity.com/paper/2502.12327/) (2025)
- [TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models](https://mlsanity.com/paper/2602.10132/) (2026)
- [Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2505.13709/) (2025)
- [Offline Reinforcement Learning for Rotation Profile Control in Tokamaks](https://mlsanity.com/paper/2605.05857/) (2026)
- [Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2604.20627/) (2026)
- [Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark](https://mlsanity.com/paper/2607.11915/) (2026)
- [Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models: A Nuclear Fusion Case Study](https://mlsanity.com/paper/2608.27578/) (2026)
- [Real-Time Plasma State Prediction via FPGA-Accelerated Quantized Recurrent Probabilistic Neural Networks](https://mlsanity.com/paper/2609.23141/) (2026)

## 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.