Fusion Foundation Model (XGC)

Domain: Plasma physics — gyrokinetic turbulence simulation (XGC) training-data prep

Source: use_cases/fusion-fm/

Demo bundle: Download .tar.gz

Preprocess HPC-scale XGC ADIOS2 BP5 simulation output into GNN-ready npz files and a PyTorch Dataset for training a plasma foundation model, via a bundled skill that registers the scripts as DSAgt codes with provenance. Advanced, bring-your-own-data.

Estimated time: ~30 minutes on an HPC login node — not a 10-minute demo and data is not included. This is an advanced, bring-your-own-data example: the XGC cases below are HPC-scale ADIOS2 BP5 output (up to ~1.3M mesh nodes) that must be supplied by the user, and the scripts require adios2 + torch plus MATEY's BaseCFDGraphDataset. Point the paths below at your own XGC run. DSAgt drives this via the bundled skill (see Skill below), which registers and runs these scripts as codes with provenance.

Domain: Plasma physics — gyrokinetic turbulence simulation
Simulation code: XGC (X-point Gyrokinetic Code)
Data format: ADIOS2 BP5 (one directory per simulation run)
Goal: Produce GNN-ready npz files and a PyTorch Dataset for training plasma surrogate models

Data

Three example simulation cases are referenced under example_xgc_data/ (not shipped in-repo — substitute your own XGC output directories):

Case	Machine	Nodes	nphi	Steps	Has f3d
n560fr_ITER_PFPO_W_Ne	ITER	1,277,797	32	99	yes (every 2)
n613fr_KSTART_30306_q4_rmp_turbulence	KSTART	45,291	32	99	yes (every 10)
ti316_NSTX_small_dt_from_ti313	NSTX	86,513	16	999	no

Each case directory contains: - xgc.mesh.bp — static 2D poloidal mesh (R/Z coordinates, triangle connectivity, flux surfaces) - xgc.3d.NNNNN.bp — per-timestep field snapshots (dpot, eden, iden, shape [nphi, n_nodes] or [n_nodes, nphi]) - xgc.f3d.NNNNN.bp — fluid moments (e_den, e_T_para/perp, e_u_para, i_* equivalents) where available

Skill

See skills/xgc-ai-training/SKILL.md for the full agent pipeline.

Quick Start (manual)

# 1. Check case structure
python skills/xgc-ai-training/scripts/check_xgc_structure.py example_xgc_data/n613fr_KSTART_30306_q4_rmp_turbulence

# 2. Summarize physics content
python skills/xgc-ai-training/scripts/xgc_summarize.py example_xgc_data/n613fr_KSTART_30306_q4_rmp_turbulence

# 3. Preprocess to npz
python skills/xgc-ai-training/scripts/xgc_preprocess.py \
    example_xgc_data/n613fr_KSTART_30306_q4_rmp_turbulence \
    /tmp/kstart_npz

# 4. Validate output
python skills/xgc-ai-training/scripts/check_xgc_preprocessed.py /tmp/kstart_npz

# 5. Use the dataset in training
python skills/xgc-ai-training/scripts/xgc_dataset.py /tmp/kstart_npz

Variable Reference

See skills/xgc-ai-training/references/xgc_fields.md.