DynamisLab/src/SR_analysis

SR_analysis: Unified SINDy-SR Analysis Pipeline

Overview

This directory consolidates the SINDy-and-symbolic-regression analysis pipeline for the DynamisLab fluidic pinball project. It replaces the old src/analysis_crossre/ and src/analysis_cloak/ directories with a unified structure.

The pipeline fits sparse interpretable control laws (obs -> act) for all cloak and illusion scenes, using dimensionless physical features, G-equivariant structural constraints, and STLSQ threshold grids.

For background, see:

• sindy_sr_notes.md -- execution plan and task tracking
• sindy_sr_knowledge.md -- confirmed facts and known pitfalls
• ../../docs/SR_analysis_results.md -- comprehensive results report

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Directory Structure

SR_analysis/
  configs.py                 # Unified scene metadata (all 10+ scenes)
  configs/
    legacy/                  # Legacy CFD configs
  utils/
    __init__.py              # Selective exports (no pycuda dependency)
    feature_builder.py       # Dimensionless features + G-operator + phase-state features
    sindy_fitter.py          # STLSQ + feature matrices + derivative/absolute modes
    cfd_interface.py         # LegacyCelerisLab wrapper (requires pycuda_3_10)
    g_operator.py            # Equivariance diagnostics
  data/
    karman/                  # Karman cloak: karman_re50/100/200/400
    steady/                  # Steady cloak
    illusion/                # Illusion: illusion_0.75L/1L/1.5L
    vortex/                  # Vortex cloak
  scripts/
    infer_karman.py          # Inference: LegacyCFD + PPO -> controlled.npz
    infer_illusion.py        # Inference for illusion scenes
    infer_vortex.py          # Inference for vortex scenes
  sindy/
    run_all_v2.py            # Unified SINDy fitting (supports --deriv, --phase, --output-mode etc.)
    run_pysr.py              # Restricted PySR symbolic regression
    wrap_joint.py            # Joint model -> wrapped format for validator
    compare_v2.py            # Cross-scene comparison report
    karman/illusion/vortex/  # SINDy output JSONs
  validate/
    run_closed_loop.py       # Karman closed-loop validator (v23/deriv/abs modes)
    run_closed_loop_illusion.py  # Illusion closed-loop validator
    eval_rollout.py          # Offline multi-step rollout evaluation
    results/                 # Validation result JSONs
  compare/
    support_overlap.py       # Support set comparison
    shared_core.py           # Shared core detection

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Key Design Decisions

1. Scene Metadata Driven

All scene parameters defined once in configs.py.

2. Feature Levels

Level	Features	Dim	Description
Static	u_m, u_a, u_c, v_a, Cd_tot, Cd_rear, Cl_tot, Cl_diff	8	Current-step only
Phase-state	u_a, du_a/dt, Cl_tot, dCl_tot/dt, Cd_tot, Cd_rear	6	Oscillation phase + rate
Illusion-phase	Phase-state + Cd_err, Cl_err, dCd_err/dt, dCl_err/dt	10	Phase + error-state
Karman-expanded	Phase-state + u_m, u_c, v_a, Cl_diff	10	Phase + supplementary
Full-lag	Static + lag-1	16	Full temporal context

3. Output Modes

• deriv: predict d(alpha)/dt, then alpha(t) = alpha(t-1) + dt_c * dalpha/dt
• absolute: predict alpha(t) directly (no integration drift)

4. G-Equivariant Structure (v23)

Front(t)  = f_front(x(t))               # no bias, odd under G
Top(t)    = f_rear(x(t))                # with bias
Bottom(t) = -f_rear(G[x(t)])            # shared-head

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Current Best Results (2026-06-15)

Illusion — New Route: Phase-state + Error-state + Absolute Action

Scene	Closed-loop	% of PPO	Action history?	Features
0.75L	0.974	100.2%	No	ILLUSION_PHASE (10dim)
1L	0.958	98.5%	No	ILLUSION_PHASE (10dim)
1.5L	N/A	—	No	Bang-bang regime

Karman re100 — Ablation

Config	Feat	Output	R2	Closed-loop	Note
old v23 (a_lag)	14+3	alpha	0.996	0.901	Baseline
Phase->abs	6	alpha	0.965	0.699	Best new route
Phase->deriv	6	dalpha/dt	0.837	0.656
Phase+mu->abs	9	alpha	0.979	0.700	mu helps cross-Re
Expanded->abs	10	alpha	0.980	0.580	Overfitting

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Commands

All from repo root (/home/frank14f/DynamisLab).

SINDy Fitting

# Illusion phase-state + absolute (recommended for 0.75L/1L)
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

# 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

# Karman expanded (10 dim)
conda run -n pycuda_3_10 python src/SR_analysis/sindy/run_all_v2.py \
    --scenes karman_re100 --deriv --karman-expand --output-mode absolute

# Karman with mu modulation
conda run -n pycuda_3_10 python src/SR_analysis/sindy/run_all_v2.py \
    --scenes karman_re100 --deriv --karman-mu --output-mode absolute

# Old-style (v2, with action history)
conda run -n pycuda_3_10 python src/SR_analysis/sindy/run_all_v2.py \
    --scenes karman_re50,karman_re100 --joint

Closed-loop Validation

# Karman with absolute action
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

# Karman old v23
conda run -n pycuda_3_10 python src/SR_analysis/validate/run_closed_loop.py \
    --scene karman_re100 --device 0 --steps 200 --mode v23 \
    --sindy-results src/SR_analysis/sindy/karman/sindy_joint_wrapped.json

# Illusion with absolute action
conda run -n pycuda_3_10 python src/SR_analysis/validate/run_closed_loop_illusion.py \
    --scene illusion_1L --device 0 --steps 320 \
    --sindy-results src/SR_analysis/sindy/illusion/sindy_results_deriv.json

PySR Symbolic Regression

conda run -n sr_env python src/SR_analysis/sindy/run_pysr.py --scene illusion_1L

Offline Rollout Evaluation

python3 src/SR_analysis/validate/eval_rollout.py \
    --sindy-results src/SR_analysis/sindy/karman/sindy_results_deriv.json \
    --scene karman_re100

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Important Reminders

• controlled.npz actions are normalized [-1,1] — must convert via (norm * scale + bias) * u0
• FIFO bias ≠ DRL action bias for Illusion: FIFO=[0, -0.01, 0.01], decode=[0, -0.02, 0.02]
• "2U" in model name = S_DIM=14 (not 2x velocity), u0 always 0.01
• SAMPLE_INTERVAL: 0.75L=400, 1L=600, 1.5L/Karman=800
• Closed-loop steps auto-set: S=400→320, S=600→214, S=800→160
• One-step R² high ≠ closed-loop good — always validate
• For phase-state features, always pass sensors_raw/forces_raw to enable derivative computation