Add 4080 grouped multi-zone states and independent mesh checks

README.md

@@ -16,6 +16,16 @@ tags:
 - scientific-machine-learning
 pretty_name: AgentFEM Programmable Soft Rods
 configs:
+- config_name: multizone_v02
+  data_files:
+  - split: train
+    path: multizone-v02/train.jsonl.gz
+  - split: validation
+    path: multizone-v02/validation.jsonl.gz
+  - split: test
+    path: multizone-v02/test.jsonl.gz
+  - split: zone_ood
+    path: multizone-v02/zone_ood.jsonl.gz
 - config_name: equilibria
   data_files:
   - split: train
@@ -30,6 +40,8 @@ configs:
 
 # AgentFEM · Programmable Soft Rods
 
+**New: [multi-zone actuation supplement](multizone-v02/README.md)** — 4,080 additional states; 3/6/12 zones, with 8-zone holdout and mesh checks. The original 16,200-state collection remains available as `equilibria`.
+
 **Learn shapes. Distinguish branches. Design actuation.**
 
 ![Three families of computed flexible-rod shapes](assets/overview.png)

multizone-v02/README.md

@@ -0,0 +1,23 @@
+# 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.

multizone-v02/mesh-checks.json

@@ -0,0 +1,172 @@
+[
+  {
+    "id": "z3_000_5",
+    "tip_difference_over_L": 4.603976254440725e-05,
+    "centerline_rmse_over_L": 6.361763189918143e-05
+  },
+  {
+    "id": "z3_020_5",
+    "tip_difference_over_L": 9.028800757149764e-06,
+    "centerline_rmse_over_L": 2.7482932927981872e-05
+  },
+  {
+    "id": "z3_040_5",
+    "tip_difference_over_L": 2.735908106962287e-05,
+    "centerline_rmse_over_L": 4.98418357228845e-05
+  },
+  {
+    "id": "z3_060_5",
+    "tip_difference_over_L": 1.6067726115273442e-05,
+    "centerline_rmse_over_L": 3.7085958702185335e-05
+  },
+  {
+    "id": "z3_080_5",
+    "tip_difference_over_L": 3.0206619534396127e-05,
+    "centerline_rmse_over_L": 5.133563955006805e-05
+  },
+  {
+    "id": "z3_100_5",
+    "tip_difference_over_L": 2.146966129776038e-05,
+    "centerline_rmse_over_L": 3.865407210995892e-05
+  },
+  {
+    "id": "z3_120_5",
+    "tip_difference_over_L": 2.2504350087581327e-05,
+    "centerline_rmse_over_L": 3.839696637125274e-05
+  },
+  {
+    "id": "z3_140_5",
+    "tip_difference_over_L": 2.061080227363198e-05,
+    "centerline_rmse_over_L": 3.5543909425014484e-05
+  },
+  {
+    "id": "z3_160_5",
+    "tip_difference_over_L": 1.692271647763019e-05,
+    "centerline_rmse_over_L": 2.3776689029657916e-05
+  },
+  {
+    "id": "z3_180_5",
+    "tip_difference_over_L": 5.545476164713359e-06,
+    "centerline_rmse_over_L": 2.0527130425934408e-05
+  },
+  {
+    "id": "z6_000_5",
+    "tip_difference_over_L": 2.4046436395802937e-05,
+    "centerline_rmse_over_L": 4.627011521342771e-05
+  },
+  {
+    "id": "z6_020_5",
+    "tip_difference_over_L": 3.182910759435842e-05,
+    "centerline_rmse_over_L": 5.040611871120139e-05
+  },
+  {
+    "id": "z6_040_5",
+    "tip_difference_over_L": 3.0365175570780548e-05,
+    "centerline_rmse_over_L": 5.245915743265558e-05
+  },
+  {
+    "id": "z6_060_5",
+    "tip_difference_over_L": 1.9946935631083388e-05,
+    "centerline_rmse_over_L": 2.8849621955517115e-05
+  },
+  {
+    "id": "z6_080_5",
+    "tip_difference_over_L": 2.136773352088528e-05,
+    "centerline_rmse_over_L": 4.1115534402941165e-05
+  },
+  {
+    "id": "z6_100_5",
+    "tip_difference_over_L": 1.207107300827187e-05,
+    "centerline_rmse_over_L": 2.9171732842140974e-05
+  },
+  {
+    "id": "z6_120_5",
+    "tip_difference_over_L": 2.115380868145924e-05,
+    "centerline_rmse_over_L": 4.204016199001519e-05
+  },
+  {
+    "id": "z6_140_5",
+    "tip_difference_over_L": 2.1235057070129793e-05,
+    "centerline_rmse_over_L": 4.234916959919376e-05
+  },
+  {
+    "id": "z6_160_5",
+    "tip_difference_over_L": 3.7047364022135785e-05,
+    "centerline_rmse_over_L": 5.7604509364778207e-05
+  },
+  {
+    "id": "z6_180_5",
+    "tip_difference_over_L": 3.4749230003974724e-05,
+    "centerline_rmse_over_L": 5.713599594208783e-05
+  },
+  {
+    "id": "z12_000_5",
+    "tip_difference_over_L": 1.8314703263704387e-05,
+    "centerline_rmse_over_L": 3.744023097267619e-05
+  },
+  {
+    "id": "z12_020_5",
+    "tip_difference_over_L": 1.8696165026924934e-05,
+    "centerline_rmse_over_L": 4.314786135595779e-05
+  },
+  {
+    "id": "z12_040_5",
+    "tip_difference_over_L": 3.660754042481668e-05,
+    "centerline_rmse_over_L": 5.734715362801281e-05
+  },
+  {
+    "id": "z12_060_5",
+    "tip_difference_over_L": 2.772976736349974e-05,
+    "centerline_rmse_over_L": 4.799696174019728e-05
+  },
+  {
+    "id": "z12_080_5",
+    "tip_difference_over_L": 2.6830417405048573e-05,
+    "centerline_rmse_over_L": 4.9781697237358266e-05
+  },
+  {
+    "id": "z12_100_5",
+    "tip_difference_over_L": 2.5053092492319243e-05,
+    "centerline_rmse_over_L": 4.6486272645130724e-05
+  },
+  {
+    "id": "z12_120_5",
+    "tip_difference_over_L": 2.880915971215596e-05,
+    "centerline_rmse_over_L": 4.896360116246912e-05
+  },
+  {
+    "id": "z12_140_5",
+    "tip_difference_over_L": 2.791571360083178e-05,
+    "centerline_rmse_over_L": 4.713340498375442e-05
+  },
+  {
+    "id": "z12_160_5",
+    "tip_difference_over_L": 2.7966535682978217e-05,
+    "centerline_rmse_over_L": 4.9353373253418014e-05
+  },
+  {
+    "id": "z12_180_5",
+    "tip_difference_over_L": 1.79296883293744e-05,
+    "centerline_rmse_over_L": 3.8050289749949345e-05
+  },
+  {
+    "id": "z8_000_5",
+    "tip_difference_over_L": 2.391581307196624e-05,
+    "centerline_rmse_over_L": 4.4834920749881116e-05
+  },
+  {
+    "id": "z8_020_5",
+    "tip_difference_over_L": 3.0930859539508996e-05,
+    "centerline_rmse_over_L": 5.10187863809428e-05
+  },
+  {
+    "id": "z8_040_5",
+    "tip_difference_over_L": 1.9119874215879227e-05,
+    "centerline_rmse_over_L": 3.49128210367896e-05
+  },
+  {
+    "id": "z8_060_5",
+    "tip_difference_over_L": 4.0496683844125254e-05,
+    "centerline_rmse_over_L": 6.214737261975e-05
+  }
+]

multizone-v02/rejected.json

@@ -0,0 +1 @@
+[]

multizone-v02/source/multizone.py

@@ -0,0 +1,84 @@
+"""Reproducible v0.2 multi-zone supplement; preserves v0.1 artifacts."""
+import os
+os.environ.setdefault('OPENBLAS_NUM_THREADS','1')
+os.environ.setdefault('OMP_NUM_THREADS','1')
+from pathlib import Path
+import json,gzip,time
+import numpy as np
+import torch
+from torch import nn
+from safetensors.torch import save_file,load_file
+from rod import Rod,Controls,serial
+
+ROOT=Path(__file__).resolve().parents[1]
+OUT=ROOT/'release/dataset/multizone-v02'
+MODEL=ROOT/'release/model/multizone-v02'
+def features(ks,chi,fx,fy):
+    field=np.asarray(ks)[np.floor((np.arange(48)+.5)*len(ks)/48).astype(int)]
+    return np.r_[field,fx/chi,fy/chi,np.log10(chi)],np.cumsum(field)/48
+def network():
+    return nn.Sequential(nn.Linear(51,96),nn.Tanh(),nn.Linear(96,96),nn.Tanh(),nn.Linear(96,48))
+def xy(z,fx,fy):
+    angle=(np.c_[np.zeros(len(z)),z[:,:-1]]+z)/2
+    stretch=1+fx[:,None]*np.cos(angle)+fy[:,None]*np.sin(angle)
+    ds=stretch[:,:,None]*np.stack([np.cos(angle),np.sin(angle)],axis=2)/48
+    return np.concatenate([np.zeros((len(z),1,2)),np.cumsum(ds,axis=1)],axis=1)
+def main():
+    start=time.time();OUT.mkdir(parents=True,exist_ok=True);MODEL.mkdir(parents=True,exist_ok=True)
+    rng=np.random.default_rng(20261015);rod=Rod(48);fine=Rod(96);rows=[];mesh=[];rejected=[]
+    for m in [3,6,12,8]:
+        total=200 if m!=8 else 80
+        order=rng.permutation(total);splits={int(j):('train' if i<140 else 'validation' if i<170 else 'test') for i,j in enumerate(order)}
+        for j in range(total):
+            split=splits[j] if m!=8 else 'zone_ood';ks=rng.uniform(-2.4,2.4,m);chi=float(10**rng.uniform(-4,-2.5));fx=float(chi*rng.uniform(0,.6));fy=float(chi*rng.uniform(-.6,.6))
+            for step,t in enumerate(np.linspace(0,1,6)):
+                k=ks*t;c=Controls(chi=chi,fx=fx*t,fy=fy*t,curvatures=tuple(k));_,seed=features(k,chi,c.fx,c.fy);s=rod.solve(c,seed)
+                row=dict(id=f'z{m}_{j:03d}_{step}',scene_id=f'z{m}_{j:03d}',split=split,zones=m,t=float(t),chi=chi,fx=c.fx,fy=c.fy,curvatures=k.tolist(),**serial(s))
+                if not s['accepted'] or s['stability']!='stable':rejected.append(row);continue
+                rows.append(row)
+                if step==5 and j%20==0:
+                    field=k[np.floor((np.arange(96)+.5)*m/96).astype(int)];f=fine.solve(c,np.cumsum(field)/96)
+                    assert f['accepted'] and f['stability']=='stable'
+                    mesh.append(dict(id=row['id'],tip_difference_over_L=float(np.linalg.norm(f['tip']-s['tip'])),centerline_rmse_over_L=float(np.sqrt(np.mean(np.sum((f['centerline']-s['centerline'])**2,axis=1))))))
+        print('Generated zones',m,'states',len(rows),flush=True)
+    for split in ['train','validation','test','zone_ood']:
+        with gzip.open(OUT/f'{split}.jsonl.gz','wt') as f:
+            for row in rows:
+                if row['split']==split:f.write(json.dumps(row)+'\n')
+    (OUT/'rejected.json').write_text(json.dumps(rejected))
+    (OUT/'mesh-checks.json').write_text(json.dumps(mesh,indent=2))
+    items=[features(r['curvatures'],r['chi'],r['fx'],r['fy']) for r in rows]
+    x=np.array([a for a,b in items],np.float32);seed=np.array([b for a,b in items],np.float32);z=np.array([r['z'] for r in rows],np.float32)
+    ix={s:np.array([i for i,r in enumerate(rows) if r['split']==s]) for s in ['train','validation','test','zone_ood']}
+    mean=x[ix['train']].mean(0);std=x[ix['train']].std(0);std[std<1e-6]=1
+    X=torch.from_numpy((x-mean)/std);Y=torch.from_numpy((z-seed)/.1)
+    torch.set_num_threads(2);torch.manual_seed(15);net=network();opt=torch.optim.Adam(net.parameters(),lr=.0015);best=float('inf');best_epoch=0
+    for epoch in range(201):
+        net.train()
+        for batch in np.array_split(rng.permutation(ix['train']),12):
+            opt.zero_grad();loss=(net(X[batch])-Y[batch]).square().mean();loss.backward();opt.step()
+        net.eval()
+        with torch.no_grad():val=float((net(X[ix['validation']])-Y[ix['validation']]).square().mean())
+        if val<best:best=val;best_epoch=epoch;weights={k:v.clone().contiguous() for k,v in net.state_dict().items()}
+        if epoch%50==0:print('Epoch',epoch,'validation',val,flush=True)
+    save_file(weights,str(MODEL/'residual.safetensors'));net.load_state_dict(load_file(str(MODEL/'residual.safetensors')))
+    with torch.no_grad():pred=seed+.1*net(X).numpy()
+    fx=np.array([r['fx'] for r in rows]);fy=np.array([r['fy'] for r in rows]);truth=xy(z,fx,fy)
+    metrics={}
+    for split,idx in ix.items():
+        metrics[split]={}
+        for name,zz in [('intrinsic_shape',seed),('learned_residual',pred)]:
+            err=np.linalg.norm(xy(zz,fx,fy)[idx]-truth[idx],axis=2)
+            metrics[split][name]={'centerline_rmse_over_L':float(np.sqrt(np.mean(err**2))),'tip_mean_over_L':float(err[:,-1].mean()),'tip_p95_over_L':float(np.quantile(err[:,-1],.95))}
+            nonzero=np.array([rows[i]['t']>0 for i in idx]);metrics[split][name]['nonzero_loading_rmse_over_L']=float(np.sqrt(np.mean(err[nonzero]**2)))
+    correction=[]
+    for i in ix['test'][5::36]:
+        r=rows[i];c=Controls(chi=r['chi'],fx=r['fx'],fy=r['fy'],curvatures=tuple(r['curvatures']))
+        corrected=rod.solve(c,pred[i]);assert corrected['accepted']
+        correction.append(dict(id=r['id'],residual=corrected['residual'],tip_error_over_L=float(np.linalg.norm(corrected['tip']-r['tip']))))
+    report=dict(version='0.2',states=len(rows),scenes=len(set(r['scene_id'] for r in rows)),counts={s:len(i) for s,i in ix.items()},zones_id=[3,6,12],zones_ood=[8],rejected=len(rejected),mesh_checks=len(mesh),max_mesh_tip_difference_over_L=max(r['tip_difference_over_L'] for r in mesh),best_epoch=best_epoch,metrics=metrics,seconds=time.time()-start)
+    report['mechanics_correction_checks']=correction
+    (MODEL/'config.json').write_text(json.dumps(dict(mean=mean.tolist(),std=std.tolist(),output_scale=.1,n=48,architecture=[51,96,96,48],features='48 piecewise intrinsic curvatures, fx/chi, fy/chi, log10(chi)',prediction='cumulative intrinsic angle + output_scale * network(standardized features)'),indent=2))
+    for p in [MODEL/'evaluation.json',OUT/'validation.json',ROOT/'reports/multizone-v02.json']:p.write_text(json.dumps(report,indent=2))
+    print(json.dumps(report,indent=2),flush=True)
+if __name__=='__main__':main()

multizone-v02/source/rod.py

@@ -0,0 +1,106 @@
+"""Conservative planar extensible rod. L=EA=1; chi=EI/(EA L^2).
+
+Rod: linear angle FE, midpoint axial elimination. Chain: Ye et al. (2026)
+cell rotations, first cell clamped; kr=chi*n. Independent implementations
+share ONLY the reduced local energy density. No contact or dynamics.
+SPDX-License-Identifier: Apache-2.0
+"""
+from dataclasses import dataclass, asdict
+import numpy as np
+from scipy.linalg import solve, eigh
+from scipy.optimize import root
+
+@dataclass
+class Controls:
+    chi: float = .001
+    fx: float = 0.
+    fy: float = 0.
+    moment: float = 0.
+    h: float = 0.
+    delta: float = .1
+    k1: float = 0.
+    k2: float = 0.
+    k3: float = 0.
+    curvatures: tuple | None = None
+
+class Rod:
+    def __init__(self, n=48, kind="rod"):
+        self.n, self.kind, self.ds = n, kind, 1/n
+        if kind == "rod":
+            self.d = n
+            A = np.zeros((n,n+1)); B = A.copy()
+            for i in range(n):
+                A[i,i:i+2] = .5
+                B[i,i:i+2] = [-n,n]
+            self.A,self.B = A[:,1:],B[:,1:]
+            self.mass = (A[:,1:].T@A[:,1:])/n
+        elif kind == "chain":
+            self.d = n-1
+            self.A = np.eye(n)[:,1:]
+            self.B = np.diff(np.eye(n),axis=0)[:,1:]*n
+            self.mass = np.eye(n-1)/n
+        else: raise ValueError(kind)
+        self.s = (np.arange(n)+.5)/n
+
+    def terms(self, z, c):
+        angle=self.A@z
+        ks=np.asarray(c.curvatures if c.curvatures is not None else [c.k1,c.k2,c.k3])
+        intrinsic=ks[np.minimum((np.arange(self.B.shape[0])+.5)*len(ks)/self.B.shape[0],len(ks)-1).astype(int)]
+        curvature=self.B@z-intrinsic
+        a=c.fx+2*c.h
+        f=a*np.cos(angle)+c.fy*np.sin(angle)
+        fp=-a*np.sin(angle)+c.fy*np.cos(angle)
+        g=4*c.h*c.delta
+        density=-f-.5*f*f+g*np.cos(angle)
+        energy=self.ds*(.5*c.chi*(curvature@curvature)+density.sum())-c.moment*z[-1]
+        local_g=-(1+f)*fp-g*np.sin(angle)
+        local_h=(1+f)*f-fp*fp-g*np.cos(angle)
+        grad=self.ds*(c.chi*self.B.T@curvature+self.A.T@local_g)
+        grad[-1]-=c.moment
+        tangent=self.ds*(c.chi*self.B.T@self.B+self.A.T@(local_h[:,None]*self.A))
+        return energy,grad,tangent
+
+    def solve(self,c,seed=None):
+        z=np.zeros(self.d) if seed is None else np.array(seed,dtype=float).copy()
+        it=0; trace=[]; fallback_nfev=0
+        for it in range(60):
+            _,r,K=self.terms(z,c)
+            trace.append(dict(iteration=it,residual_inf=float(np.max(np.abs(r)))))
+            if np.max(np.abs(r))<1e-10: break
+            try: dz=solve(K,-r,assume_a='sym')
+            except Exception: break
+            old=np.linalg.norm(r)
+            step=1.
+            for _ in range(16):
+                trial=z+step*dz
+                if np.linalg.norm(self.terms(trial,c)[1])<old: break
+                step*=.5
+            else: break
+            z=trial
+            trace[-1]['accepted_step_scale']=step
+        residual=float(np.max(np.abs(self.terms(z,c)[1])))
+        if residual>1e-9:
+            sol=root(lambda v:self.terms(v,c)[1],z,jac=lambda v:self.terms(v,c)[2],tol=1e-10)
+            z=sol.x; fallback_nfev=sol.nfev
+        energy,r,K=self.terms(z,c)
+        residual=float(np.max(np.abs(r)))
+        angle=self.A@z
+        stretch=1+(c.fx+2*c.h)*np.cos(angle)+c.fy*np.sin(angle)
+        admissible=bool(np.min(stretch)>(2*c.delta if c.h!=0 else 0))
+        good=bool(residual<1e-9 and admissible and np.all(np.isfinite(z)))
+        eig=float(eigh(K,self.mass,subset_by_index=[0,0],eigvals_only=True)[0]/c.chi) if good else None
+        stability='unknown' if eig is None else ('stable' if eig>1e-4 else 'unstable' if eig< -1e-4 else 'critical')
+        segments=self.ds*stretch[:,None]*np.column_stack([np.cos(angle),np.sin(angle)])
+        xy=np.vstack([np.zeros(2),np.cumsum(segments,axis=0)])
+        # Output resampled on reference coordinate, not current arc length.
+        center=np.column_stack([np.interp(np.linspace(0,1,65),np.linspace(0,1,self.n+1),xy[:,j]) for j in range(2)])
+        return dict(z=z,centerline=center,angle=angle,strain=stretch-1,energy=float(energy),residual=residual,
+                    iterations=it+1,accepted=good,lambda_min_over_chi=eig,stability=stability,
+                    tip=center[-1],min_stretch=float(stretch.min()),solver_trace=trace,fallback_nfev=fallback_nfev)
+
+    def mode_seed(self,amplitude):
+        s=np.arange(1,self.d+1)/self.n
+        return amplitude*np.sin(np.pi*s/2)
+
+def serial(result):
+    return {k:(v.tolist() if isinstance(v,np.ndarray) else v) for k,v in result.items()}

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