license: cc-by-4.0
language: []
pretty_name: 'Exp 3B: Embedded topology in trained recurrent operators'
size_categories:
- 10K<n<100K
tags:
- dynamical-systems
- delay-embedding
- reservoir-computing
- persistent-homology
- recurrent-neural-networks
- topology
viewer: false
Exp 3B: Embedded topology in trained recurrent operators
A 14,553-configuration computational sweep of recurrent network architectures trained on dynamical systems. Each configuration's hidden-state activations were measured for topological fidelity to the driving system using persistent homology and Gauss linking integrals. Six post-hoc analyses on saved checkpoints probe the operator properties the embedding theorems describe abstractly.
This dataset is the empirical companion to the manuscript "Topological reconstruction in trained recurrent operators: a 14,553-configuration empirical sweep" on arXiv to follow).
Scope
- 7 architecture families: VanillaRNN, GRU, LSTM, ESN, Mamba, RWKV, Transformer.
- 9 hidden dimensions: 3, 5, 8, 16, 32, 64, 128, 256, 512.
- 3 depths: 1, 2, 4.
- 7 tasks: circle ($S^1$), torus ($T^2$), torus3 ($T^3$), scalar circle, scalar torus, Lorenz, four-dimensional Qi system.
- 11 random seeds per cell.
- Total: 14,553 trained configurations.
Layout
master_results.json aggregate of all per-config results
master_results.jsonl streaming form of master_results.json
gpu_co2_report.{md,json} compute footprint (GPU-hours, kWh, CO2)
batch_summary.json per-batch aggregated post-hoc metrics
figures/ 20 PNG figures from the analysis pipeline
code/ analysis pipeline source
configs/<task>/<run_id>/
results.json training output (loss, topology metrics)
post_analysis.json spectral radius + collision + novel-traj
dyn_variants.json altered-dynamics evaluation
lorenz_ic_sweep.json Lorenz only: 8-IC robustness
cross_task.json cross-task transfer evaluation
gs_convergence.json empirical ESP convergence test
history.json training curve + per-eval-epoch topology
epoch_*.pt final-epoch checkpoint
Hostnames in source data have been mapped to opaque labels
(host_A_a2000, host_B_rtx4000, etc.) so the deposit does not
leak machine identity.
Reproducibility
Source code for the training and analysis pipeline is under code/.
Re-running requires PyTorch, NumPy, scipy, and ripser. See
code/scripts/ for individual analyses and
code/experiments/exp3b_taxonomy.py for the master training script.
License
CC BY 4.0. Cite the accompanying manuscript when reusing.