Atomizer/studies/m1_mirror_adaptive_V13

M1 Mirror Pure NSGA-II FEA Optimization V13

Pure multi-objective FEA optimization with NSGA-II sampler for the M1 telescope mirror support system.

Created: 2025-12-09 Protocol: Pure NSGA-II Multi-Objective (No Neural Surrogate) Status: Running

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1. Purpose

V13 runs pure FEA optimization without any neural surrogate to establish ground-truth Pareto front. This serves as:

1. Baseline for evaluating GNN/MLP surrogate accuracy
2. Ground truth Pareto front for comparison
3. Validation data for future surrogate training

Key Difference from V11/V12

Aspect	V11 (Adaptive MLP)	V12 (GNN + Validation)	V13 (Pure FEA)
Surrogate	MLP (4-layer)	Zernike GNN	None
Sampler	TPE	NSGA-II	NSGA-II
Trials/hour	~100 NN + 5 FEA	~5000 GNN + 5 FEA	6-7 FEA
Purpose	Fast exploration	Field prediction	Ground truth

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2. Seeding Strategy

V13 seeds from all prior FEA data in V11 and V12:

V11 (107 FEA trials) + V12 (131 FEA trials) = 238 total
                                               │
                                    ┌──────────┴──────────┐
                                    │  Parameter Filter   │
                                    │  (blank_backface    │
                                    │   4.0-5.0 range)    │
                                    └──────────┬──────────┘
                                               │
                                    217 trials seeded into V13

Why 21 Trials Were Skipped

V13 config uses blank_backface_angle: [4.0, 5.0] (intentionally narrower). Trials from V10/V11 with blank_backface_angle < 4.0 (range was 3.5-5.0) were rejected by Optuna.

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3. Mathematical Formulation

3.1 Objectives (Same as V11/V12)

Objective	Goal	Formula	Target	Units
rel_filtered_rms_40_vs_20	minimize	RMS_filt(Z_40 - Z_20)	4.0	nm
rel_filtered_rms_60_vs_20	minimize	RMS_filt(Z_60 - Z_20)	10.0	nm
mfg_90_optician_workload	minimize	RMS_J1-J3(Z_90 - Z_20)	20.0	nm

3.2 Design Variables (11)

Parameter	Bounds	Units
lateral_inner_angle	[25.0, 28.5]	deg
lateral_outer_angle	[13.0, 17.0]	deg
lateral_outer_pivot	[9.0, 12.0]	mm
lateral_inner_pivot	[9.0, 12.0]	mm
lateral_middle_pivot	[18.0, 23.0]	mm
lateral_closeness	[9.5, 12.5]	mm
whiffle_min	[35.0, 55.0]	mm
whiffle_outer_to_vertical	[68.0, 80.0]	deg
whiffle_triangle_closeness	[50.0, 65.0]	mm
blank_backface_angle	[4.0, 5.0]	deg
inner_circular_rib_dia	[480.0, 620.0]	mm

---

4. NSGA-II Configuration

sampler = NSGAIISampler(
    population_size=50,
    crossover=SBXCrossover(eta=15),
    mutation=PolynomialMutation(eta=20),
    seed=42
)

NSGA-II performs true multi-objective optimization:

• Non-dominated sorting for Pareto ranking
• Crowding distance for diversity preservation
• No scalarization - preserves full Pareto front

---

5. Study Structure

m1_mirror_adaptive_V13/
├── 1_setup/
│   ├── model/                    # NX model files (from V11)
│   └── optimization_config.json  # Study config
├── 2_iterations/
│   └── iter{N}/                  # FEA working directories
│       ├── *.prt, *.fem, *.sim   # NX files
│       ├── params.exp            # Parameter expressions
│       └── *solution_1.op2       # Results
├── 3_results/
│   └── study.db                  # Optuna database
├── run_optimization.py           # Main entry point
└── README.md                     # This file

---

6. Usage

# Start fresh optimization
python run_optimization.py --start --trials 55

# Resume after interruption (Windows update, etc.)
python run_optimization.py --start --trials 35 --resume

# Check status
python run_optimization.py --status

Expected Runtime

• ~8-10 min per FEA trial
• 55 trials ≈ 7-8 hours overnight

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7. Trial Sources in Database

Source Tag	Count	Description
V11_FEA	5	V11-only FEA trials
V11_V10_FEA	81	V11 trials inherited from V10
V12_FEA	41	V12-only FEA trials
V12_V10_FEA	90	V12 trials inherited from V10
FEA	10+	New V13 FEA trials

Query trial sources:

SELECT value_json, COUNT(*)
FROM trial_user_attributes
WHERE key = 'source'
GROUP BY value_json;

---

8. Post-Processing

Extract Pareto Front

import optuna

study = optuna.load_study(
    study_name="m1_mirror_V13_nsga2",
    storage="sqlite:///3_results/study.db"
)

# Get Pareto-optimal trials
pareto = study.best_trials

# Print Pareto front
for t in pareto:
    print(f"Trial {t.number}: {t.values}")

Compare to GNN Predictions

# Load V13 FEA Pareto front
# Load GNN predictions from V12
# Compute error: |GNN - FEA| / FEA

---

9. Results (To Be Updated)

Metric	Value
Seeded trials	217
New FEA trials	TBD
Pareto front size	TBD
Best rel_rms_40	TBD
Best rel_rms_60	TBD
Best mfg_90	TBD

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10. Cross-References

• V10: ../m1_mirror_zernike_optimization_V10/ - Original LHS sampling
• V11: ../m1_mirror_adaptive_V11/ - MLP adaptive surrogate
• V12: ../m1_mirror_adaptive_V12/ - GNN field prediction

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Generated by Atomizer Framework. Pure NSGA-II for ground-truth Pareto optimization.