Sophelio/fusion-equilibrium-challenge

⚛️ The Fusion Equilibrium Challenge

Predict the shape of a fusion plasma (the magnetic equilibrium, $\psi$) from control inputs and diagnostics alone — without magnetic sensors. Data comes from two tokamaks:

• DIII-D — General Atomics tokamak (San Diego, USA)
• MAST — Mega Ampere Spherical Tokamak (Culham, UK)

In data-science terms this is an image-regression / control problem: predict a 2-D poloidal flux map (efit_psirz) at each EFIT timestep from coil currents (the actuators) and Thomson-scattering temperature/density profiles (the sensors).

One row per shot. All time-series, profiles, and flux maps are stored as nested arrays within that row. Every time array is in milliseconds (both machines).

Splits & configs

Each (machine, role) is its own config, so every config has a clean schema — the two machines have different coil layouts, and the test configs genuinely omit the target column rather than null-filling it:

Config	Split	Shots	Contents
diii_d_train	train	7,290	Full — includes all targets (efit_psirz, EFIT scalars, dsep)
diii_d_public_test	public_test	911	Inputs only — all EFIT-derived columns withheld
mast_public_test	public_test	1,207	Inputs only — all EFIT-derived columns withheld

from datasets import load_dataset

REPO = "Sophelio/fusion-equilibrium-challenge"

# DIII-D training data (with targets)
train = load_dataset(REPO, "diii_d_train", split="train")

# Public test (inputs only — predict efit_psirz at each efit_times step)
d3d_test  = load_dataset(REPO, "diii_d_public_test", split="public_test")
mast_test = load_dataset(REPO, "mast_public_test",  split="public_test")

import numpy as np
shot = train[0]
psirz = np.array(shot["efit_psirz"])   # (T, 65, 65) for DIII-D
times = np.array(shot["efit_times"])   # (T,) ms — predict a flux map at each

Notes on the design:

• No MAST training data. Cross-machine generalization (DIII-D → MAST) is zero-shot by construction — the mast config has only a test split.
• All EFIT-derived quantities are withheld on the test splits. Anything computed by the EFIT reconstruction leaks the equilibrium you must predict, so every one is removed from public_test: the flux map efit_psirz, the EFIT scalars (efit_beta_n, efit_li, efit_q95, efit_r_axis, efit_z_axis, plus the auxiliary/boundary scalars), and magnetics_dsep (the x-point gap — EFIT-derived, so a target despite its magnetics_ prefix). Only the coordinate scaffolding is kept — efit_times and efit_grid_R/Z (both machines) — so you know exactly which timestamps and grid to predict on.
• A private test split is held back entirely for final scoring and is not part of this dataset.

The targets — what you predict

The primary target is the 2-D flux map. Alongside it we ship a set of scored EFIT scalar labels (one value per efit_times step) that summarise the equilibrium — these are present in train and withheld on the test splits, same as the flux map.

Key	Shape	Description
efit_psirz	(T, 65, 65) (both machines)	Primary target — poloidal flux map, a 2-D image at each timestep (V·s/rad). Withheld on test.
efit_beta_n	(T,)	Normalised beta (scored scalar). Withheld on test.
efit_li	(T,)	Internal inductance ℓi (scored scalar). Withheld on test.
efit_q95	(T,)	Safety factor at 95% flux surface (scored scalar). Withheld on test.
efit_r_axis / efit_z_axis	(T,)	Magnetic-axis R/Z (m) (scored scalars). Withheld on test.
magnetics_dsep	(T,)	X-point gap (m); >0 diverted, <0 limited. EFIT-derived — a prediction target, not an input. Withheld on test.
(boundary — both machines) efit_lcfs_n, efit_lcfs_r, efit_lcfs_z	(T,) / (T, N)	LCFS boundary contour + valid-point count — provided in train as bonus context. Withheld on test.
(aux — MAST only) efit_beta_p, efit_elongation, efit_psi_axis, efit_psi_boundary, efit_geom_axis_r/z	(T,)	Extra EFIT scalars — present on MAST only (the DIII-D fetch did not include them). Withheld on test.

dsep scoring & NaN/sentinel frames. dsep is scored as an additional scalar (R²_dsep), separate from the composite metric. It is only defined when the plasma has an active x-point; limited/startup frames are undefined, and on MAST they appear in the training labels as NaN or a −1.0 gap sentinel. The scorer excludes those frames (~40% on MAST), and you should mask them before training on dsep. On DIII-D dsep is defined throughout.

NaN frames in the scored scalars. Like dsep, the five scored EFIT scalars can be NaN on frames with incomplete reconstruction — routinely a few startup/rampdown frames per shot on MAST, and a small number of frames on a handful of DIII-D shots (whose aEQDSK scalars sat on a slightly offset EFIT time base). Mask NaN frames before training or scoring on these scalars; the scorer excludes them. All five scalars share the same valid frames, so a single NaN mask covers them.

Scaffolding (kept on every split):

Key	Shape	Description
efit_times	(T,)	Timestamps (ms) for the target images. Align all inputs to these times.
efit_grid_R / efit_grid_Z	(65,)	Physical R/Z (m) labelling the flux-map columns/rows (both machines).

Both machines ship a clean 65×65 flux map with no NaNs. (MAST's upstream EFIT stores psirz on a doubled 129-column R grid — 65 real columns interleaved with 64 empty ones; this is not a central-column hole but a grid artifact. We drop the empty columns to recover the dense 65×65 grid the data is actually defined on: MAST R ∈ [0.06, 2.0] m, Z ∈ [−2.0, 2.0] m.)

Inputs

DIII-D actuators — 18 shaping coils magnetics_F{1-9}{A,B} (±10 kA), magnetics_ECOILA (ohmic/central solenoid), magnetics_bcoil (toroidal field), magnetics_plasma_current (Ip). MAST actuators — 10 poloidal coils magnetics_p{2-6}{l,u}_current, magnetics_sol_current, magnetics_tf_current, magnetics_efps_current, magnetics_plasma_current (Ip).

Both machines — Thomson scattering core + edge profiles (thomson_core_*, thomson_edge_*: electron temperature Te in eV and density ne in m⁻³, with time bases). Each system carries a single spatial-coordinate array, and the axis differs by machine: thomson_core_R is radius (DIII-D constant ≈ 1.94 m vertical chord; MAST per-channel R), and thomson_edge_spatial is Z on DIII-D (≈ −0.05 m) but R on MAST (≈ 1.3–1.5 m). There is no separate *_z/tan_z column.

Note: magnetics_dsep (the x-point gap) used to be listed here as an input. It is EFIT-derived and is now a prediction target — present in train, withheld on test.

Time bases: each machine exposes a shared magnetics_time; on DIII-D, Ip sits on its own ADC (magnetics_plasma_current_times). In train, magnetics_dsep_times equals efit_times on every shot (it and magnetics_dsep are withheld on test).

👉 Full signal dictionary, machine differences, and a beginner-friendly guide are in the challenge's hacker_resources/ bundle (README + MODELING_GUIDE.md + fusion_data_provider.py), distributed alongside the challenge.

License & acknowledgement

Work supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the DIII-D National Fusion Facility, a DOE Office of Science user facility, under Award No. DE-FC02-04ER54698, and Awards DE-SC0024426, DE-SC0024499, DE-SC0024409, and DE-SC0024571. See the full challenge README for the complete DOE disclaimer. License: see challenge organizers (Sophelio).

Citation

@dataset{fusion_equilibrium_challenge,
  title  = {The Fusion Equilibrium Challenge: Predicting Plasma Shape from Control Inputs},
  author = {Michoski, Craig and Waller, Mathew and Sammuli, Brian and Boyes, William
            and Clark, Mitchell and Smith, Sterling and Nakkina, Tapan Ganatma
            and Hatch, David and Nazikian, Raffi},
  year   = {2026},
  note   = {Sophelio and General Atomics}
}