Atomizer/studies/M1_Mirror/m1_mirror_turbo_V2

M1 Mirror Turbo V2 - CMA-ES with Increased 40-20 Weight

Changes from V1

Setting	V1	V2
Sampler	TPE	CMA-ES
40-20 WFE weight	5.0	6.0
60-20 WFE weight	5.0	5.0
MFG 90 weight	3.0	3.0
Mass weight	1.0	0.0 (disabled)

Purpose

V1 achieved WS=282.05 but 40-20 WFE slightly degraded vs V11 baseline (6.55 vs ~6.5 nm).

V2 changes:

1. CMA-ES sampler - Better for continuous optimization, exploits gradients more effectively than TPE
2. Increased 40-20 weight - Prioritize the 40-20 WFE objective
3. Disabled mass - Since mass constraint is always satisfied and changes are minimal

Baseline

• V1 Best: WS=282.05 (trial 28)
• Starting surrogate: Trained on 45 FEA samples from V1

Quick Start

cd studies/M1_Mirror/m1_mirror_turbo_V2

# Run optimization
python run_optimization.py --start

# Or with custom settings
python run_optimization.py --start --max-fea 100 --patience 5

# Test single FEA
python run_optimization.py --test

Expected Behavior

Since mass is disabled (weight=0) and 40-20 is increased (6.0 vs 5.0):

• Optimization will focus more on reducing 40-20 WFE
• Mass may increase slightly (but stay under 105kg constraint)
• Best WS number will be different from V1 (different weights = different scale)

Comparing V1 vs V2

Do NOT compare WS values directly - they use different weight scales.

Compare individual objectives:

• 40-20 WFE (nm) - should be lower in V2
• 60-20 WFE (nm)
• MFG 90 (nm)

Files

• 1_setup/optimization_config.json - Configuration with V2 weights
• 3_results/checkpoints/best_model.pt - Surrogate from V1 (45 samples)
• 3_results/gnn_data/fea_samples.json - Training data from V1
• run_optimization.py - Main script (updated for V2 weights)