awesome-ml-in-plasma-physics

DOI - We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learing (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments,...

DOI - Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's duration. Existing tools like reinforcement learning, supervised learning, and Bayesian optimization address some of these challenges but fail to provide a comprehensive solution. To overcome these li...

DOI - This paper presents the development and experimental validation of a reinforcement learning (RL)-based magnetic controller on the DIII-D tokamak. The controller directly maps raw magnetic diagnostic signals to actuator commands, replacing the traditional isoflux control algorithm based on equilibrium reconstruction. Four RL controllers are trained using the Soft Actor–Critic algorithm with an asymmetric Actor–Critic architecture in the NSFsim simulator. All controllers are deployed in the DII...

DOI - Accurately predicting plasma behavior based on discharge configurations is essential for the safe and efficient operation of tokamak experiments. While physics-based integrated modeling codes provide valuable insights, their high computational cost limits their applicability for fast scenario design and control optimization. In this study, we propose a transformer-based machine learning model to predict key global plasma parameters on the Tungsten (W) Environment in Steady-State Tokamak (WEST...

DOILink - We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks. An autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements, and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its c...

DOI - Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of ${\gt}200,000$ nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turb...

Integrated inter-disciplinary design tool for future fusion reactors with modules for plasma physics, engineering, and optimization (LGPL-2.1)

Python/Rust quasi-steady electromagnetics toolkit covering filament models, Biot-Savart calculations, and Grad-Shafranov utilities

Commonwealth Fusion Systems public repositories for SPARC physics inputs, POPCON analysis, electromagnetics, and scientific-software utilities

Plasma Operating CONtour analysis tool for tokamak performance prediction and optimization

Proxima Fusion dataset of QI-like stellarator boundaries, ideal-MHD equilibria, and optimization benchmarks (

Stellarator equilibrium and optimization suite using pseudo-spectral methods and automatic differentiation

Code - Neural network model for cross-device line-integral diagnostics with physics constraints