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Atomizer/studies/m1_mirror_adaptive_V15/STUDY_REPORT.md
Antoine 01a7d7d121 docs: Complete M1 mirror optimization campaign V11-V15
## M1 Mirror Campaign Summary
- V11-V15 optimization campaign completed (~1,400 FEA evaluations)
- Best design: V14 Trial #725 with Weighted Sum = 121.72
- V15 NSGA-II confirmed V14 TPE found optimal solution
- Campaign improved from WS=129.33 (V11) to WS=121.72 (V14): -5.9%

## Key Results
- 40° tracking: 5.99 nm (target 4.0 nm)
- 60° tracking: 13.10 nm (target 10.0 nm)
- Manufacturing: 26.28 nm (target 20.0 nm)
- Targets not achievable within current design space

## Documentation Added
- V15 STUDY_REPORT.md: Detailed NSGA-II results analysis
- M1_MIRROR_CAMPAIGN_SUMMARY.md: Full V11-V15 campaign overview
- Updated CLAUDE.md, ATOMIZER_CONTEXT.md with NXSolver patterns
- Updated 01_CHEATSHEET.md with --resume guidance
- Updated OP_01_CREATE_STUDY.md with FEARunner template

## Studies Added
- m1_mirror_adaptive_V13: TPE validation (291 trials)
- m1_mirror_adaptive_V14: TPE intensive (785 trials, BEST)
- m1_mirror_adaptive_V15: NSGA-II exploration (126 new FEA)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2025-12-16 14:55:23 -05:00

11 KiB
Raw Blame History

M1 Mirror Adaptive V15 - NSGA-II Multi-Objective Optimization Report

Study: m1_mirror_adaptive_V15 Algorithm: NSGA-II (Multi-Objective Genetic Algorithm) Status: Completed Created: 2025-12-12 Completed: 2025-12-15


Executive Summary

V15 applied NSGA-II multi-objective optimization to explore the Pareto front of design trade-offs, seeded with 494 valid trials from V14. The campaign completed 126 new FEA evaluations but did not find improvements over the V14-optimized designs.

Metric Value
Total Trials in DB 644
Seeded from V14 494
New V15 FEA Trials 126
Failed Trials 24
Pareto Front Size 2
Best Weighted Sum 121.72 (from V14 seed)
Best V15 FEA Weighted Sum 127.35

Key Finding: NSGA-II exploration confirmed that V14 TPE optimization had already converged to the optimal region. No V15 FEA trial improved upon the seeded V14 data.


1. Optimization Configuration

1.1 Algorithm Settings

Parameter Value
Algorithm NSGA-II
Population Size 50
Crossover Probability 0.9
Mutation Adaptive
Seed 42

1.2 Design Variables (11 total)

Variable Min Max Baseline Best Value Units
lateral_inner_angle 25.0 30.0 26.79 27.88 deg
lateral_outer_angle 11.0 17.0 14.64 13.19 deg
lateral_outer_pivot 9.0 12.0 10.40 11.53 mm
lateral_inner_pivot 5.0 12.0 10.07 7.41 mm
lateral_middle_pivot 15.0 27.0 20.73 22.95 mm
lateral_closeness 9.5 12.5 11.02 9.93 mm
whiffle_min 30.0 72.0 40.55 58.90 mm
whiffle_outer_to_vertical 60.0 80.0 75.67 77.84 deg
whiffle_triangle_closeness 50.0 80.0 60.00 66.88 mm
blank_backface_angle 4.1 4.5 4.15 4.30 deg
inner_circular_rib_dia 480.0 620.0 534.00 505.89 mm

1.3 Objectives

Objective Description Target Direction Weight
rel_filtered_rms_40_vs_20 40° tracking error vs 20° ref 4.0 nm minimize 5
rel_filtered_rms_60_vs_20 60° tracking error vs 20° ref 10.0 nm minimize 5
mfg_90_optician_workload Manufacturing deformation (J1-J3 filtered) 20.0 nm minimize 1

Weighted Sum Formula: 5 × obj_40 + 5 × obj_60 + 1 × obj_mfg


2. Results Analysis

2.1 Best Design (Global - from V14 Seed)

Trial #445 (originally V14 Trial #725)

Objective Value Target Status
40° vs 20° tracking 5.99 nm 4.0 nm Above target
60° vs 20° tracking 13.10 nm 10.0 nm Above target
Manufacturing (90°) 26.28 nm 20.0 nm Above target
Weighted Sum 121.72 - Best achieved

2.2 Best V15 FEA Design

Trial #631 (New V15 FEA evaluation)

Objective Value vs Best Delta
40° vs 20° tracking 5.92 nm 5.99 nm -1.2% ✓
60° vs 20° tracking 13.78 nm 13.10 nm +5.2% ✗
Manufacturing (90°) 28.83 nm 26.28 nm +9.7% ✗
Weighted Sum 127.35 121.72 +4.6% ✗

2.3 Top 10 Designs Overall

Rank Trial Source 40°/20° (nm) 60°/20° (nm) Mfg (nm) WS
1 #445 V14_FEA_725 5.99 13.10 26.28 121.72
2 #444 V14_FEA_724 6.05 13.17 26.42 122.54
3 #274 V14_FEA_550 5.76 13.27 27.53 122.69
4 #440 V14_FEA_720 6.09 13.34 26.96 124.12
5 #438 V14_FEA_716 6.08 13.37 27.17 124.42
6 #271 V14_FEA_547 5.81 13.43 28.36 124.57
7 #290 V14_FEA_566 5.85 13.48 28.03 124.66
8 #275 V14_FEA_551 5.83 13.50 28.06 124.67
9 #487 V14_FEA_778 6.24 13.41 26.55 124.77
10 #364 V14_FEA_642 5.89 13.50 27.95 124.91

Observation: All top 10 designs are from V14 seeded data. No V15 FEA trial made it into the top 10.

2.4 Top 5 V15 FEA Trials

Rank Trial 40°/20° (nm) 60°/20° (nm) Mfg (nm) WS
1 #631 5.92 13.78 28.83 127.35
2 #540 6.60 14.08 27.82 131.21
3 #621 6.01 14.10 31.08 131.63
4 #570 6.07 14.31 31.35 133.28
5 #624 5.98 14.47 32.22 134.45

2.5 Pareto Front Analysis

The true Pareto front (non-dominated solutions) contains only 2 designs:

Trial Source 40°/20° (nm) 60°/20° (nm) Mfg (nm) WS
#445 V14_FEA_725 5.99 13.10 26.28 121.72
#274 V14_FEA_550 5.76 13.27 27.53 122.69

Interpretation: The small Pareto front indicates the objectives are not strongly conflicting in the optimal region. Trial #274 trades slightly worse 60° performance and manufacturing for better 40° performance.


3. Source Distribution

Source Count Percentage
V14 Seeded 494 76.7%
V15 FEA (new) 126 19.6%
V15 FEA (failed) 24 3.7%
Total 644 100%

4. Why NSGA-II Did Not Improve

  1. V14 TPE Was Highly Effective: The 785 TPE trials in V14 (including V11-V13 seeds) already explored the design space extensively and found a strong local/global optimum.

  2. Converged Design Space: The best designs cluster tightly in parameter space, indicating convergence to an optimal region.

  3. NSGA-II Exploration vs Exploitation: NSGA-II prioritizes Pareto front diversity over single-objective convergence. It sampled widely but the promising regions were already well-explored.

  4. Small Pareto Front: With only 2 truly non-dominated solutions, there's limited trade-off space to explore.


5. Comparison with Prior Versions

Version Algorithm FEA Trials Best WS Best 40° Best 60° Best Mfg
V11 GNN + TuRBO 107 129.33 6.34 13.81 28.54
V12 GNN + TuRBO 5131* 129.33 6.34 13.81 28.54
V13 TPE 291 129.33 6.34 13.81 28.54
V14 TPE (adaptive) 785 121.72 5.99 13.10 26.28
V15 NSGA-II 126 121.72** 5.99** 13.10** 26.28**

* V12 includes 5000+ surrogate predictions ** Best from seeded V14 data, not new V15 FEA

Campaign Improvement: From V11 baseline to V14/V15 best:

  • 40° tracking: 6.34 → 5.99 nm (-5.5%)
  • 60° tracking: 13.81 → 13.10 nm (-5.1%)
  • Manufacturing: 28.54 → 26.28 nm (-7.9%)
  • Weighted Sum: 129.33 → 121.72 (-5.9%)

6. Best Design Summary

The best design found across the entire M1 mirror optimization campaign (V11-V15):

Trial: V14 #725 (V15 #445)
Source: FEA Validated

Design Variables:
  lateral_inner_angle:        27.8846°
  lateral_outer_angle:        13.1862°
  lateral_outer_pivot:        11.5290 mm
  lateral_inner_pivot:         7.4063 mm
  lateral_middle_pivot:       22.9518 mm
  lateral_closeness:           9.9300 mm
  whiffle_min:                58.8986 mm
  whiffle_outer_to_vertical:  77.8388°
  whiffle_triangle_closeness: 66.8829 mm
  blank_backface_angle:        4.3030°
  inner_circular_rib_dia:    505.8876 mm

Performance:
  40° vs 20° tracking:     5.99 nm (target: 4.0 nm)
  60° vs 20° tracking:    13.10 nm (target: 10.0 nm)
  Manufacturing (90°):    26.28 nm (target: 20.0 nm)
  Weighted Sum:          121.72

6.2 Target Achievement Status

Objective Target Achieved Gap Status
40° tracking 4.0 nm 5.99 nm +50% Not met
60° tracking 10.0 nm 13.10 nm +31% Not met
Manufacturing 20.0 nm 26.28 nm +31% Not met

Conclusion: Current design space constraints cannot achieve the specified targets. Consider:

  1. Expanding design variable bounds
  2. Adding new design variables
  3. Relaxing targets based on physical limitations
  4. Design modifications (geometry changes, materials)

7. Conclusions and Recommendations

7.1 Key Conclusions

  1. Optimization Converged: The M1 mirror optimization campaign has effectively converged after ~1,400 total FEA evaluations across V11-V15.

  2. V14 TPE Found Optimal: The V14 TPE optimization found the best design; V15 NSGA-II exploration confirmed this optimum.

  3. Limited Trade-offs: The small Pareto front (2 solutions) indicates the three objectives are not strongly conflicting in the optimal region.

  4. Targets Not Achievable: Current design space cannot achieve the specified targets. The best achievable performance is ~50% above targets.

7.2 Recommendations

  1. Accept Current Best: Trial #725 (V14) / #445 (V15) represents the optimal design within current constraints.

  2. Archive for Production: Run python tools/archive_best_design.py m1_mirror_adaptive_V15 to archive the best design.

  3. Future Work Options:

    • Expand design space (new variables, wider bounds)
    • Alternative support structures
    • Material optimization
    • Active correction systems
  4. No Further Optimization Needed: Additional trials are unlikely to find significant improvements without design space changes.


Appendix A: File Locations

studies/m1_mirror_adaptive_V15/
├── 1_setup/
│   ├── optimization_config.json    # Study configuration
│   └── model/                      # NX model files
├── 2_iterations/
│   └── iter*/                      # FEA iteration folders
├── 3_results/
│   ├── study.db                    # Optuna database
│   ├── pareto_front.json           # Pareto front data
│   └── optimization.log            # Execution log
├── run_optimization.py             # Main optimization script
└── STUDY_REPORT.md                 # This report

Appendix B: Zernike Analysis

Subcase Configuration

Subcase Angle Description
1 90° Manufacturing/polishing position
2 20° Reference position (minimum gravity)
3 40° Operational tracking position
4 60° Operational tracking position

Filtering Strategy

  • Tracking (40°, 60° vs 20°): Filter Zernike modes J0-J3 (piston, tip/tilt, focus)
  • Manufacturing (90° vs 20°): Filter only J1-J3 (tip/tilt, focus)
  • Number of Zernike modes: 50

Report generated by Atomizer. Last updated: 2025-12-15