awesome-ml-in-plasma-physics

Automatic-Differentiation-Enabled Plasma Transport code in JAX, enabling differentiable simulation and neural network training pipelines for kinetic and fluid plasma models (

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 (

DOI - In this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing cap...

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 - 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 - We have developed TorbeamNN: a machine learning surrogate model for the TORBEAM ray tracing code to predict electron cyclotron heating (ECH) and current drive locations in tokamak plasmas. TorbeamNN provides more than a 100 times speed-up compared to the highly optimized and simplified real-time implementation of TORBEAM without any reduction in accuracy compared to the offline, full fidelity TORBEAM code. The model was trained using KSTAR ECH mirror geometries and works for both O-mode and X...

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...

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