# Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas

arXiv: https://arxiv.org/abs/2312.01286 · HTML: https://mlsanity.com/paper/2312.01286/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Long Range Arena: A Benchmark for Efficient Transformers](https://mlsanity.com/paper/2011.04006/) (2020)
- [FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes](https://mlsanity.com/paper/2110.08059/) (2021)
- [Modelling Long Range Dependencies in ND: From Task-Specific to a General Purpose CNN](https://mlsanity.com/paper/2301.10540/) (2023)
- [Hybrid deep learning architecture for general disruption prediction across tokamaks](https://mlsanity.com/paper/2007.01401/) (2020)
- [A general framework for quantifying uncertainty at scale](https://mlsanity.com/paper/2202.03999/) (2022)

## Built on by (most cited first, 2 of 2)

- [Time Series Viewmakers for Robust Disruption Prediction](https://mlsanity.com/paper/2410.11065/) (2024)
- [Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms](https://mlsanity.com/paper/2609.24443/) (2026)

## Abstract (arXiv)

Grid decarbonization for climate change requires dispatchable carbon-free energy like nuclear fusion. The tokamak concept offers a promising path for fusion, but one of the foremost challenges in implementation is the occurrence of energetic plasma disruptions. In this study, we delve into Machine Learning approaches to predict plasma state outcomes. Our contributions are twofold: (1) We present a novel application of Continuous Convolutional Neural Networks for disruption prediction and (2) We examine the advantages and disadvantages of continuous models over discrete models for disruption prediction by comparing our model with the previous, discrete state of the art, and show that continuous models offer significantly better performance (Area Under the Receiver Operating Characteristic Curve = 0.974 v.s. 0.799) with fewer parameters