Datasets:

stark4062
/

plantforge

license: cc-by-4.0
task_categories:
  - time-series-forecasting
  - other
tags:
  - system-identification
  - control-theory
  - dynamics
  - synthetic
  - benchmark
  - nonlinear-dynamics
size_categories:
  - 100K<n<1M
pretty_name: PLANTFORGE

PLANTFORGE corpus

A procedural control-plant corpus for in-context system identification, organized along three axes jointly: nonlinearity family × excitation class × sampling rate (exact ZOH), with per-instance Fisher-information identifiability annotations.

Code, generator, training/evaluation pipeline, and full documentation: https://github.com/Soarr01/plantforge (see docs/DATASHEET.md there for the full Datasheet-for-Datasets writeup this card summarizes).

Quick facts

240,000 instances: 5 families × 4 excitations × 3 rates × 4000 instances/cell.
Families: stribeck (velocity friction) · backlash (input deadzone) · saturate (input clipping) · boucwen (output hysteresis) · drivetrain (two-inertia motor/gear/compliant-load).
Excitations: prbs · multisine · chirp · closedloop (true sequential PI loop, not a two-pass imitation).
Rates: 10 / 20 / 50 Hz, exact zero-order-hold from one shared continuous-time truth per instance (state-nonlinear families substep internally so dt and dt/4 agree at common instants).
Ground truth: named physical parameters per instance, no hidden normalization (verified: re-simulation from θ reproduces y to 1e-6).
Identifiability annotations: per-parameter relative Cramér-Rao lower bound and FIM log10-condition-number, per (instance, excitation, rate). Note an honest caveat from the release experiments: these annotations do not positively predict in-context prediction difficulty (within-cell median Spearman r ≈ −0.09 against per-instance prediction nMSE, robust to confound controls) — parameter-recovery difficulty decouples from prediction difficulty. They are intended as metadata for excitation-design and identifiability studies, not as a difficulty score.

Format

60 shard files ({family}_{excitation}_dt{rate}hz.pt, PyTorch tensor dicts), plus registry.json. Each shard:

import torch
shard = torch.load("stribeck_multisine_dt50hz.pt")
shard["u"], shard["y"]          # (T, B) input/output trajectories
shard["theta"]                  # (B, K) named physical parameters
shard["keys"]                   # length-K parameter names
shard["rel_crlb"]               # (B, K) per-parameter relative CRLB
shard["log10_cond"]             # (B,) log10 FIM condition number
shard["dt"], shard["family"], shard["excitation"]   # shard metadata

Updates

2026-07-27: corrected the rel_crlb/log10_cond identifiability annotations. The generator's identifiability() function previously used torch.linalg.pinv on the regularized Fisher information matrix, which silently returned rel_crlb=0 for any parameter with near-zero sensitivity to the excitation (e.g. sat when the excitation never reaches the saturation limit) instead of a large value — the opposite of correct CRLB semantics (zero sensitivity means maximally unidentifiable, which should show a large rel_crlb, not a near-zero one). This affected 9.3% of all 240,000 rows (46.4% of the saturate family). The annotations were recomputed in place with a corrected regularized inverse; u, y, and theta are byte-identical to the previous revision — only rel_crlb and log10_cond changed. The previous revision remains addressable via its commit SHA on the Files and versions tab.

License

CC BY 4.0 — see LICENSE-DATA in the code repository, or https://creativecommons.org/licenses/by/4.0/. Attribution: cite the code repository (https://github.com/Soarr01/plantforge) and this dataset.

Citation

<BibTeX — TBD, pending paper draft>