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SR Analysis: Phase-state SINDy + ablation study + documentation

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Core changes:
- New phase-state features (PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS) with obs dynamics
- Derivative and absolute output modes (output_mode="deriv"|"absolute")
- predict_v23_deriv() with integration support for closed-loop
- Offline multi-step rollout evaluator (eval_rollout.py)

Key results:
- Illusion 0.75L/1L: phase-state+error-state+abs achieves 0.974/0.958 closed-loop
  with zero action history features — proving the new route works
- Karman re100: phase-state+abs reaches 0.699 (vs 0.901 with action history)
- 1.5L confirmed as bang-bang regime (R2=0.12 for linear SINDy)
- Feature ablation: 6-dim phase-state outperforms 16-dim full-lag in closed-loop

Documentation:
- docs/SR_analysis_results.md: comprehensive analysis report
- docs/HANDOVER_SR_ANALYSIS.md: handover notes for next coder
- 6 figures in docs/figures/SR_analysis/
- Updated README.md, sindy_sr_notes.md, sindy_sr_knowledge.md
- Updated configs.py with generalization scenes

Co-authored-by: Cursor <cursoragent@cursor.com>
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Frank14f
2026-06-22 16:55:03 +08:00
co-authored by Cursor
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docs/HANDOVER_SR_ANALYSIS.md
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# SR Analysis Pipeline — Handover Notes (2026-06-15)
## 交接人 → 接手人
### 当前管线状态
| 模块 | 状态 | 说明 |
|------|:----:|------|
| Illusion 0.75L + 1L phase-state + abs | **已验证** | 闭环 0.974 / 0.958，无动作历史，可进 PySR |
| Illusion 1.5L | **边界 case** | bang-bang 机制，线性 SINDy 不适用 |
| Karman phase-state + abs | **0.699** | 优于 deriv 模式，但低于旧 v23 的 0.901 |
| Karman old v23 (a_lag) | **0.901** | 保留作基线对照 |
| Karman 泛化测试 | **已完成** | Re70/150/300/25 约 0.54-0.60 |
| PySR 符号回归 | **有 shell** | 需要修复 run_pysr.py 后重新跑 |
| Vortex 偏移扩展 | **未做** | 低优先级 |
### 核心文件改动（2026-06-14~15）
| 文件 | 改动类型 |
|------|----------|
| `utils/feature_builder.py` | **新增** PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS, KARMAN_EXPANDED_KEYS, obs dynamics, error-state, mu_Cl_tot |
| `utils/sindy_fitter.py` | **新增** compute_action_deriv, get_feature_matrix_deriv(output_mode) |
| `sindy/run_all_v2.py` | **新增** --deriv, --phase, --karman-expand, --karman-mu, --output-mode, --augment-level CLI |
| `validate/run_closed_loop.py` | **新增** predict_v23_deriv, mode="abs", load_sindy_coefs返回mode |
| `validate/run_closed_loop_illusion.py` | **修改** 支持 predict_v23_deriv, 自动检测模式 |
| `validate/eval_rollout.py` | **新文件** 离线多步 rollout 评估 |
| `scripts/plot_sr_results.py` | **新文件** 结果可视化图表 |
| `docs/SR_analysis_results.md` | **新文件** 完整分析报告 |
| `docs/figures/SR_analysis/fig*.png` | **新文件** 6 张图表 |
### 关键设计决策（接手前必读）
1. **phase-state 特征** = `u_a, du_a/dt, Cl_tot, dCl_tot/dt, Cd_tot, Cd_rear` (6维)
2. **ILLUSION_PHASE_KEYS** = phase-state + `Cd_err, Cl_err, dCd_err/dt, dCl_err/dt` (10维)
3. **绝对动作输出** `output_mode="absolute"` 优于导数 `"deriv"`，无积分累积
4. **v23 结构**始终默认：front no-bias, rear shared-head
5. **时间一阶导**统一除以 `dt_c = SAMPLE_INTERVAL/2000`，跨场景可比较
6. `controlled.npz` 中新加了 `target_forces` 字段，illusion 场景必须有
7. **FIFO bias ≠ DRL action bias**，1U vs 2U 不要混淆
### 常用命令速查
```bash
# 拟合 + 验证 Illusion phase-state + absolute (完整流程)
conda run -n pycuda_3_10 python src/SR_analysis/sindy/run_all_v2.py \
--scenes illusion_0.75L,illusion_1L --deriv --phase --output-mode absolute
conda run -n pycuda_3_10 python src/SR_analysis/validate/run_closed_loop_illusion.py \
--scene illusion_0.75L --device 0 --steps 320 \
--sindy-results src/SR_analysis/sindy/illusion/sindy_results_deriv.json
# 拟合 + 验证 Karman phase-state + absolute
conda run -n pycuda_3_10 python src/SR_analysis/sindy/run_all_v2.py \
--scenes karman_re100 --deriv --phase --output-mode absolute
conda run -n pycuda_3_10 python src/SR_analysis/validate/run_closed_loop.py \
--scene karman_re100 --device 0 --steps 200 --mode abs \
--sindy-results src/SR_analysis/sindy/karman/sindy_results_deriv.json
# 离线 rollout 评估
python3 src/SR_analysis/validate/eval_rollout.py \
--sindy-results src/SR_analysis/sindy/karman/sindy_results_deriv.json \
--scene karman_re100
# PySR (需要先修复滞后的 bug！)
conda run -n sr_env python src/SR_analysis/sindy/run_pysr.py --scene illusion_1L
# 画图
python3 scripts/plot_sr_results.py
```
### 目前最适合推进的方向
1. **Illusion PySR 符号回归**（0.75L + 1L separate → 公式比较）
2. **Karman 状态补强**（配合 CCD/OID 分析找出缺失的状态量，再回 SR）
3. **Karman 跨 Re 联合**（在 phase-state + mu 基础上做跨 Re 联合拟合 + 泛化）
### 注意重新运行 run_pysr.py
当前 `run_pysr.py` 有路径/导入问题，接手后需先确认：
- `env_sr` 或 `sr_env` 环境内的 PySR 可用性
- whitelist 特征与 `controlled.npz` 中的字段匹配（特别是 illusion 的 `target_forces`）
- 滞后构造正确（`a_prev[1:] = actions_phys[:-1]`）
docs/SR_analysis_results.md
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# SR Analysis: Phase-State SINDy Results Report
> Date: 2026-06-15
> Project: DynamisLab — Active hydrodynamic cloaking and illusion using DRL on a fluidic pinball.
> Analysis pipeline: SINDy (STLSQ) + feature engineering for interpretable control law extraction.
---
## Executive Summary
Three key findings from the SR analysis pipeline:
1. **Illusion 0.75L + 1L: New route validated.** Phase-state features + error-state + absolute action output achieves closed-loop similarity of **0.96+** (97% of PPO) with **zero action history features**. This proves that physically meaningful control laws can be extracted without relying on action memory.
2. **Illusion 1.5L: Regime shift identified.** The 1.5L target exhibits bang-bang/saturated control (alpha range [-8, 8], autocorrelation r=0.07). Linear SINDy is fundamentally inadequate (R2=0.12). This is a regime boundary, not a modelling failure.
3. **Karman: State representation still incomplete.** The same phase-state approach reaches 0.699 (vs 0.901 baseline with action history). The problem is not the output form (derivative vs absolute) but insufficient input state information for the Karman scene.
---
## 1. Methodology
### 1.1 Feature Architecture (Final)
Three feature levels were tested for fitting `alpha = f(state)` or `d(alpha)/dt = g(state)`:
| Level | Features | Dim | Description |
|-------|----------|:---:|-------------|
| Static | u_m, u_a, u_c, v_a, Cd_tot, Cd_rear, Cl_tot, Cl_diff | 8 | Current-step physics only, no memory |
| Phase-state | u_a, du_a/dt, Cl_tot, dCl_tot/dt, Cd_tot, Cd_rear (+error terms) | 6+4 | Oscillation phase + rate + drag feedback |
| Full-lag | Static + lag-1 of all 8 | 16 | Brute-force temporal context (baseline) |
### 1.2 Output Modes
Two output targets were compared:
- **Derivative mode**: predict d(alpha)/dt, then integrate `alpha(t) = alpha(t-1) + dt_c * dalpha/dt`
- **Absolute mode**: predict alpha directly, no integration needed
### 1.3 Evaluation Metrics
Models are evaluated on:
1. **One-step R2**: fit quality on training data
2. **Offline multi-step rollout**: 1/5/20/50 step recursive prediction on held-out PPO data
3. **CFD closed-loop**: full CFD environment with SINDy control law, DTW similarity vs target
---
## 2. Illusion Results
### 2.1 Three-Scene Summary
| Scene | S | Old v23 (a_lag) | New phase+error+abs | PPO baseline | % of PPO | Features | Action history? |
|------|:--:|:----------------:|:-------------------:|:------------:|:--------:|----------|:---------------:|
| 0.75L | 400 | 0.908 | **0.974** | 0.972 | 100.2% | 10-dim (ILLUSION_PHASE) | **No** |
| 1L | 600 | 0.962 | **0.958** | 0.973 | 98.5% | 10-dim (ILLUSION_PHASE) | **No** |
| 1.5L | 800 | 0.926 | **N/A** | 0.945 | — | — | Bang-bang regime |
**Key insight**: The 0.75L scene's new route **outperforms** the old v23 (0.974 vs 0.908), while 1L matches it within 1.5%. This definitively proves that "action history is not necessary."
### 2.2 Illusion 1L Front Coefficients (Phase-State, Absolute Output)
```
alpha_F = f(u_a, du_a/dt, Cl_tot, dCl_tot/dt, Cd_tot, Cd_rear, Cd_err, Cl_err, dCd_err/dt, dCl_err/dt)
R2 = 0.987
```
Key contributors (sorted by |coef|): Cd_rear > Cd_tot > Cd_err > Cl_tot > Cl_err > dCl_err/dt > dCd_err/dt
The formula is dominated by **drag-based feedback** (Cd_tot, Cd_rear, Cd_err), with the oscillation phase (u_a, Cl_tot) providing secondary modulation.
### 2.3 Illusion 1.5L — Regime Shift Evidence
| Metric | 1L | 1.5L | Ratio |
|--------|:--:|:----:|:-----:|
| Alpha range (front) | [-0.36, 1.11] | **[-8.0, 8.0]** | 10x |
| 1-step autocorrelation | 0.957 | **0.065** | — |
| d(alpha)/dt std | 0.12 | **6.07** | 50x |
| Linear SINDy R2 | 0.987 | **0.124** | — |
The 1.5L controller operates at saturation limits (maximum rotation speed), flipping between extremes. This is consistent with the frequency-doubling control strategy reported in the confirmation report.
---
## 3. Karman Results
### 3.1 Ablation Study (re100)
| Configuration | Feat | Output | R2 | Closed-loop | % of PPO |
|--------------|:----:|:-----:|:--:|:----------:|:--------:|
| old v23 (a_lag1 dominant) | 14+3 | alpha | 0.996 | **0.901** | 94.4% |
| **Phase-state -> abs (best new)** | **6** | **alpha** | **0.965** | **0.699** | 73.3% |
| Phase-state -> deriv | 6 | dalpha/dt | 0.837 | 0.656 | 68.8% |
| Phase-state + mu -> abs | 9 | alpha | 0.979 | 0.700 | 73.4% |
| Expanded 10-dim -> abs | 10 | alpha | 0.980 | **0.580** | 60.8% |
| Full-lag -> deriv | 16 | dalpha/dt | 0.939 | 0.619 | 64.9% |
| Static -> deriv | 8 | dalpha/dt | 0.321 | 0.745 | 78.1% |
**Key insight from ablation**:
1. **Phase-state + absolute output is the best new route** (0.699), but still well below the action-history baseline.
2. Adding extra static features (expanded 10-dim) **hurts** closed-loop despite higher R2 — classic covariate shift.
3. The static->deriv paradox: low R2 (0.321) but good closed-loop (0.745), because no-memory models are naturally robust to rollout divergence.
4. Mu modulation doesn't help at single-Re; its value will appear in cross-Re fitting.
### 3.2 Cross-Re Generalization (old v23 model)
| Re | Type | Closed-loop | Note |
|:--:|:----:|:----------:|------|
| 50 | Training | 0.582 | Low-frequency shedding |
| 100 | Training | **0.901** | Default / best |
| 200 | Training | 0.793 | Moderate degradation |
| 400 | Training | 0.664 | High-Re challenge |
| 25 | Unseen (subcritical) | 0.567 | Below Hopf bifurcation |
| 70 | Unseen | 0.577 | Between Re50 and Re100 |
| 150 | Unseen | 0.595 | Between Re100 and Re200 |
| 300 | Unseen | 0.541 | Outer extrapolation |
### 3.3 One-Step R2 vs Closed-Loop Paradox
A key phenomenon discovered during this work: **high one-step R2 does not predict good closed-loop performance** when temporal features (lags, derivatives) are present in the input. This is because:
- **Training**: features use ground-truth PPO observations
- **Deployment**: features use SINDY-controlled observations (distribution shift)
- Temporal features amplify this shift recursively
---
## 4. Roadmap to Next Steps
### 4.1 Illusion: Proceed to PySR
The 0.75L and 1L scenes are ready for symbolic regression. Recommended input:
```python
ILLUSION_PHASE_KEYS = [
"u_a", "du_a_dt", # oscillation phase
"Cl_tot", "dCl_tot_dt", # lift dynamics
"Cd_tot", "Cd_rear", # drag feedback
"Cd_err", "Cl_err", # force error
"dCd_err_dt", "dCl_err_dt", # error dynamics
]
```
Output: absolute alpha (non-dimensional action), no integration.
First do separate PySR for each scene, then compare formula structure for shared backbone.
### 4.2 Karman: CCD/OID for state completion
The 0.699 ceiling suggests missing state variables. Candidate directions:
- Recirculation zone length / reattachment point
- Wake centerline deflection
- POD mode coefficients for phase completion
- CCD modes correlated with control action
### 4.3 SR / CCD / OID Integration Framework
```
┌─────────────────────────────────────┐
│ Control Objective │
│ (stealth / illusion / erase) │
└──────────┬──────────────────────────┘
│
┌──────────▼──────────┐
│ DRL Policy │
│ (PPO + Sin act) │
└──────────┬──────────┘
│
┌────────────────┼────────────────┐
│ │ │
┌────────▼──────┐ ┌─────▼──────┐ ┌──────▼─────────┐
│ SINDy / SR │ │ CCD / OID │ │ Validation │
│ obs -> act │ │ obs -> z │ │ CFD closed- │
│ white-box │ │ structure │ │ loop + DTW │
│ control law │ │ analysis │ │ │
└────────┬──────┘ └─────┬──────┘ └──────┬─────────┘
│ │ │
└────────────────┼─────────────────┘
│
┌──────────▼──────────┐
│ Interpretable │
│ Control Mechanics │
│ obs -> z -> act │
│ -> structure -> │
│ -> signature │
└─────────────────────┘
```
---
## 5. Code Changes Summary
### Files Modified (8 core files)
| File | Changes |
|------|---------|
| `src/SR_analysis/utils/feature_builder.py` | PHYSICS_FEAT_KEYS, ILLUSION_ERR_KEYS, PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS, KARMAN_EXPANDED_KEYS; obs dynamics (lag1 + derivative); error-state computation; 1D target_forces support |
| `src/SR_analysis/utils/sindy_fitter.py` | compute_action_deriv(); get_feature_matrix_deriv() with output_mode="deriv"|"absolute"; include_mu support |
| `src/SR_analysis/sindy/run_all_v2.py` | run_single_scene_deriv(); run_joint_karman_deriv(); --deriv, --phase, --karman-expand, --karman-mu, --output-mode, --augment-level CLI |
| `src/SR_analysis/validate/run_closed_loop.py` | predict_v23_deriv() with output_mode; mode="abs" branch; load_sindy_coefs returns "mode" |
| `src/SR_analysis/validate/run_closed_loop_illusion.py` | Support predict_v23_deriv; auto-detect SINDy mode from coefs |
| `src/SR_analysis/utils/__init__.py` | Export new constants and functions |
| `src/SR_analysis/sindy/wrap_joint.py` | Parameterized for karman/illusion |
| `src/SR_analysis/validate/eval_rollout.py` | **New**: offline multi-step rollout evaluation |
### Key Design Principles
1. **No action history in features** for all new routes (PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS)
2. **Time-normalized derivatives** for cross-scene compatibility: `dx/dt = (x(t) - x(t-1)) / dt_c`
3. **Error-state** for Illusion: Cd_err, Cl_err encode current-to-target deviation
4. **Output flexibility**: both derivative and absolute modes supported
---
## 6. Figures
All figures in `docs/figures/SR_analysis/`:
- **fig1_illusion_comparison.png**: Illusion 3-scene comparison bar chart
- **fig2_karman_ablation.png**: Karman re100 ablation across 7 configurations
- **fig3_karman_generalization.png**: Karman cross-Re generalization (training vs unseen)
- **fig4_r2_vs_closedloop.png**: One-step R2 vs closed-loop paradox scatter
- **fig5_illusion_coefficients.png**: Illusion 1L front feature coefficients
- **fig6_roadmap.png**: Research progress roadmap
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