multizone-v02/README.md

# Multi-zone actuation · v0.2 supplement

4,080 equilibrium states in 680 independent control scenarios. This supplement adds 3, 6 and 12 actuation regions to one continuous, planar extensible rod. Eight-region scenarios are a held-out zone-count test. It does not represent connected robots, contact or physical-time dynamics.

## Protocol

- 200 scenarios each at 3, 6 and 12 zones; 80 additional scenarios at 8 zones.
- Each scenario has six loading levels t=0,0.2,...,1; all six stay in the same split.
- Train: 2,520 states; validation: 540; test: 540; zone-count OOD: 480.
- Curvature controls sampled uniformly in [-2.4,2.4]; chi log-uniform in [1e-4,10^-2.5]. Final fx/chi in [0,0.6], fy/chi in [-0.6,0.6]. Curvature and forces ramp together with t. L=EA=1, zero end moment and zero microforce h.
- A 48-element angle-FE model generates locally stable equilibrium states. 34 final states are checked against 96 elements. Maximum checked endpoint difference: 4.604e-5 L.
- Zero-loading states are repeated across scenarios by construction; they are not evidence of generalization. Evaluation reports both all-state and nonzero-loading errors. Grouping prevents a nonzero loading path from crossing splits.
- Eight-zone OOD is a limited zone-count shift, not arbitrary-geometry or load extrapolation.

Each compressed JSONL record contains scene and split IDs, zone count, controls, loading parameter, angle DOFs, a 65-point reference-coordinate centerline, strain, endpoint, residual, local eigenvalue and Newton trace. Rejected samples are retained in rejected.json (none in this release). Full metrics and mesh checks accompany the data.

Reproduce with `OPENBLAS_NUM_THREADS=1 python multizone.py` from the original project layout. Source files are supplied in `source/`; they resolve the project root as their parent directory's parent. Dependencies: NumPy, SciPy, PyTorch and safetensors. Generation and training use seed 20261015.

## 中文

新增 680 组驱动工况、4,080 个平衡状态。3、6、12 分区用于训练与同分布测试；8 分区全部留作测试。同一工况的加载状态不跨数据划分。数据描述连续柔性杆的理想化曲率驱动，不是实物机器人实验数据。

Data: CC BY 4.0. Source code: Apache-2.0. Original v0.1 files remain unchanged.