gerkone/pinc_gkw

Gyrokinetics compression test set (PINC)

Held-out 5D gyrokinetics test set and neural field checkpoints to reproduce the compression evaluation in Physics-Informed Neural Compression of High-Dimensional Plasma Data (PINC). Links to the paper, code, and authors are withheld for double-blind review; see the anonymized submission for details.

Data

60 held-out turbulent trajectories of adiabatic-electron gyrokinetic simulations (GKW), stored in float32:

• steady state: timesteps 90-260 (step 10) of every trajectory, 1080 snapshots (main results table);
• transition: 10 snapshots across the transition window of 47 trajectories, 470 snapshots (temporal-consistency table); windows and timesteps in transition_windows.json.

The electrostatic potentials are the field solve of the stored distribution function, and the per-trajectory potential statistics in metadata_light.pkl are computed from them.

iteration_<n>_ifft_realpotens/
  data/timestep_<t>.bin   # 5D distribution function f at timestep t
  data/poten_<t>.bin      # electrostatic potential at timestep t
  metadata_light.pkl      # geometry, grid, spectra metadata
transition_windows.json   # transition window (t0, t1, dt) and evaluated timesteps per trajectory
checkpoints/
  nf/                     # steady-state neural fields, one per snapshot
    best_mlp_*.pt         #   NF
    best_int_mlp_*.pt     #   PINC-NF
  nf_transition/          # transition-window neural fields (same naming)
  pinn/best_int_mlp_*.pt  # PINN baseline neural fields (steady state)

Neural field file names encode trajectory, timestep and compression ratio: <prefix>_<trajectory>_t<timestep>_x<compression ratio>.pt.

Tables (dataset viewer)

• index/snapshots.csv: one row per stored snapshot (1080 steady + 470 transition): trajectory, timestep (file index), time (R/V_r), phase (steady / transition), the physical parameters ion_temp_grad, density_grad, s_hat, q, the heat_flux at that time, the paths of the df_file and potential_file, and of the nf_checkpoint, pinc_nf_checkpoint and pinn_checkpoint (steady state only) for that snapshot.
• index/trajectories.csv: one row per trajectory (60): physical parameters, number of saved timesteps, steady snapshots and their mean heat flux, and the transition window (transition_t0, transition_t1).

Usage

Download the snapshots and checkpoints and run the evaluation pipeline provided with the anonymized code submission, e.g.

python scripts/run_eval1k.py --methods nf,nf-pinc,sz3,jpeg2000,zfp \
    --ckpts <local>/checkpoints/nf --path <local>
python scripts/run_eval1k.py --methods nf,nf-pinc \
    --ckpts <local>/checkpoints/nf_transition --path <local>

Changelog

• 2026-10: potentials replaced by the field solve of the stored f (the previous ones were shifted), potential statistics updated; neural field checkpoints updated to the reported models; added the transition snapshots and the PINN and transition neural fields; snapshot and trajectory index tables.