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 - 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...

DOIAIP - A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from operation of the DIII-D tokamak using extreme ultraviolet / vacuum ultraviolet (EUV/VUV) emission spectroscopy matched with high-accuracy divertor Thomson scattering measurements of the electron temperature, $T_e$. This NN is shown to be particularly good at predic...

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...

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 - 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,...

First open-source foundation model for tokamak plasma dynamics, pretrained on MAST heterogeneous diagnostics

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