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Antoine 8cbdbcad78 feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements

## Protocol 13: Adaptive Multi-Objective Optimization
- Iterative FEA + Neural Network surrogate workflow
- Initial FEA sampling, NN training, NN-accelerated search
- FEA validation of top NN predictions, retraining loop
- adaptive_state.json tracks iteration history and best values
- M1 mirror study (V11) with 103 FEA, 3000 NN trials

## Dashboard Visualization Enhancements
- Added Plotly.js interactive charts (parallel coords, Pareto, convergence)
- Lazy loading with React.lazy() for performance
- Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB)
- Chart library toggle (Recharts default, Plotly on-demand)
- ExpandableChart component for full-screen modal views
- ConsoleOutput component for real-time log viewing

## Documentation
- Protocol 13 detailed documentation
- Dashboard visualization guide
- Plotly components README
- Updated run-optimization skill with Mode 5 (adaptive)

## Bug Fixes
- Fixed TypeScript errors in dashboard components
- Fixed Card component to accept ReactNode title
- Removed unused imports across components

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

Co-Authored-By: Claude <noreply@anthropic.com>

2025-12-04 07:41:54 -05:00

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M1 Mirror Adaptive Surrogate Optimization V11 - Results Report

Study: m1_mirror_adaptive_V11 Generated: 2025-12-03 12:14:21 Status: Running

Executive Summary

Metric	Value
V10 FEA Trials (Training Data)	90
V11 FEA Trials (Validation)	1
V11 NN Trials (Surrogate)	1000
Total Trials	1091
Current Iteration	2
Best 40-20 Operational	6.10 nm
Best 60-20 Operational	14.02 nm
Best Manufacturing (90-20)	29.82 nm
Best Weighted Objective	1.4661

1. Study Configuration

Objectives (Relative Filtered RMS)

Objective	Description	Weight	Target	Units
`rel_filtered_rms_40_vs_20`	Filtered RMS WFE at 40 deg relative to 20 deg reference (operational tracking)	5.0	4.0	nm
`rel_filtered_rms_60_vs_20`	Filtered RMS WFE at 60 deg relative to 20 deg reference (operational tracking)	5.0	10.0	nm
`mfg_90_optician_workload`	Manufacturing deformation at 90 deg polishing (J1-J3 filtered RMS)	1.0	20.0	nm

Design Variables

Parameter	Min	Max	Baseline	Units
lateral_inner_angle	25.0	28.5	26.79	degrees
lateral_outer_angle	13.0	17.0	14.64	degrees
lateral_outer_pivot	9.0	12.0	10.4	mm
lateral_inner_pivot	9.0	12.0	10.07	mm
lateral_middle_pivot	18.0	23.0	20.73	mm
lateral_closeness	9.5	12.5	11.02	mm
whiffle_min	35.0	55.0	40.55	mm
whiffle_outer_to_vertical	68.0	80.0	75.67	degrees
whiffle_triangle_closeness	50.0	65.0	60.0	mm
blank_backface_angle	3.5	5.0	4.23	degrees
inner_circular_rib_dia	480.0	620.0	534.0	mm

2. Optimization Progress

Current State

Iteration: 2
Total FEA Evaluations: 100
Total NN Evaluations: 2000
Convergence Patience: 5 iterations

Iteration History

Iter	FEA Count	NN Count	Best 40-20	Best 60-20	Best Mfg	Improved
1	95	1000	6.10	14.02	29.82	No
2	100	2000	6.10	14.02	29.82	No

3. Results Summary

FEA Trials Statistics

Metric	40-20 (nm)	60-20 (nm)	Mfg (nm)
Minimum	5.97	13.81	28.54
Maximum	7.97	19.13	59.36
Mean	6.73	15.63	37.81
Std Dev	0.42	1.04	6.45

Neural Network Trials Statistics

No NN trials with objective values available yet.

4. Best Designs Found

Best Overall Design

Weighted Objective: 1.4661

Objective	Value	Target	Status
40-20 Operational	6.10 nm	4.0 nm	FAIL
60-20 Operational	14.02 nm	10.0 nm	FAIL
Manufacturing (90-20)	29.82 nm	20.0 nm	FAIL

Design Parameters:

Parameter	Value	Unit
lateral_inner_angle	27.1328	degrees
lateral_outer_angle	13.8125	degrees
lateral_outer_pivot	10.9219	mm
lateral_inner_pivot	10.0781	mm
lateral_middle_pivot	18.3906	mm
lateral_closeness	10.5781	mm
whiffle_min	47.1875	mm
whiffle_outer_to_vertical	73.8125	degrees
whiffle_triangle_closeness	52.5781	mm
blank_backface_angle	3.8516	degrees
inner_circular_rib_dia	613.4375	mm

5. Neural Surrogate Performance

Training Configuration

Setting	Value
Architecture	MLP [128, 256, 256, 128, 64]
Dropout	0.1
Batch Size	16
Learning Rate	0.001
MC Dropout Samples	30

Model Checkpoints

File	Description
`surrogate_initial.pt`	Surrogate model checkpoint
`surrogate_iter1.pt`	Surrogate model checkpoint
`surrogate_iter2.pt`	Surrogate model checkpoint

6. Trial Source Distribution

Source	Count	Percentage
V10_FEA (Training)	90	8.2%
V11_FEA (Validation)	1	0.1%
V11_NN (Surrogate)	1000	91.7%

Speedup Analysis

Metric	FEA	Neural	Ratio
Trial Count	91	1000	11x
Est. Time per Trial	~5 min	~10 ms	~30,000x

7. Engineering Recommendations

Optical Performance Analysis

Based on the optimization results:

40-20 Tracking: NEEDS IMPROVEMENT - Above target (6.10 nm, target: 4.0 nm)
60-20 Tracking: GOOD - Close to target (14.02 nm, target: 10.0 nm)
Manufacturing: GOOD - Close to target (29.82 nm, target: 20.0 nm)

Next Steps

If optimization is running: Monitor convergence in the dashboard
If converged: Validate best design with detailed FEA analysis
If targets not met: Consider:
- Expanding design variable ranges
- Adding more design variables
- Increasing FEA validation budget

8. Files Generated

File	Description
`3_results/study.db`	Optuna database with all trials
`3_results/adaptive_state.json`	Iteration-by-iteration state
`3_results/surrogate_*.pt`	Neural model checkpoints
`3_results/optimization.log`	Detailed execution log

9. Dashboard Visualization

Trial Source Differentiation

Trial Type	Marker	Color
FEA	Circle	Blue (#2196F3)
NN	Cross	Orange (#FF9800)

Access Points

Dashboard	URL	Purpose
Atomizer Dashboard	http://localhost:3000	Real-time monitoring, charts
Optuna Dashboard	http://localhost:8081	Trial history, Pareto analysis

Report auto-generated by Atomizer V11 Report Generator Last updated: 2025-12-03 12:14:21

5.7 KiB Raw Blame History