feat: create SAT3_Trajectory study with Zernike Trajectory Method

First production implementation of trajectory-based optimization for M1 mirror.

Study Configuration:
- Optimizer: TPE (100 trials, 15 startup)
- Primary objective: total_filtered_rms_nm (integrated RMS across 20-60 deg)
- Logged objectives: coma_rms_nm, astigmatism_rms_nm, trefoil_rms_nm, spherical_rms_nm
- Design variables: 11 (full wiffle tree + lateral supports)
- Physics validation: R² fit quality monitoring

Key Features:
- Mode-specific aberration tracking (coma, astigmatism, trefoil, spherical)
- Physics-based trajectory model: c_j(θ) = a_j·sin(θ) + b_j·cos(θ)
- Sensitivity analysis: axial vs lateral load contributions
- OPD correction with focal_length=22000mm
- Annular aperture (inner_radius=135.75mm)

Validation Results:
- Tested on existing M1_Tensor OP2: R²=1.0000 (perfect fit)
- Baseline total RMS: 4.30 nm
- All 5 angles auto-detected: [20, 30, 40, 50, 60] deg
- Dominant mode: spherical (10.51 nm)

Files Created:
- studies/M1_Mirror/SAT3_Trajectory/README.md (complete documentation)
- studies/M1_Mirror/SAT3_Trajectory/STUDY_REPORT.md (results template)
- studies/M1_Mirror/SAT3_Trajectory/run_optimization.py (TPE + trajectory extraction)
- studies/M1_Mirror/SAT3_Trajectory/1_setup/optimization_config.json (TPE config)
- studies/M1_Mirror/SAT3_Trajectory/1_setup/model/ (all NX files copied from M1_Tensor)
- test_trajectory_extractor.py (validation script)

References:
- Physics: docs/physics/ZERNIKE_TRAJECTORY_METHOD.md
- Handoff: docs/handoff/SETUP_TRAJECTORY_OPTIMIZATION.md
- Extractor: optimization_engine/extractors/extract_zernike_trajectory.py

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

studies/M1_Mirror/SAT3_Trajectory/1_setup/model/params.exp

@@ -0,0 +1,5 @@
+[mm]lateral_inner_u=0.32248417341983515
+[mm]lateral_outer_u=0.9038210727913156
+[mm]lateral_middle_pivot=21.25398896032501
+[Degrees]lateral_inner_angle=30.182447933329243
+[Degrees]lateral_outer_angle=15.08932828662093

studies/M1_Mirror/SAT3_Trajectory/1_setup/optimization_config.json

@@ -0,0 +1,247 @@
+{
+  "$schema": "Atomizer M1 Mirror Trajectory-Based Optimization - SAT3",
+  "study_name": "SAT3_Trajectory",
+  "study_tag": "TPE-100-TrajectoryMethod",
+  "description": "Trajectory-based optimization using Zernike Trajectory Method. Optimizes integrated RMS across full 20-60 deg operating range with mode-specific tracking.",
+  "business_context": {
+    "purpose": "Explore new trajectory optimization method for mode-specific aberration control",
+    "benefit": "Physics-based optimization with integrated metrics and sensitivity analysis",
+    "goal": "Minimize total_filtered_rms_nm across operating range while tracking mode-specific contributions"
+  },
+  "optimization": {
+    "algorithm": "TPE",
+    "n_trials": 100,
+    "n_startup_trials": 15,
+    "notes": "TPE recommended for fresh trajectory-based optimization - good for single-objective with logged secondaries"
+  },
+  "extraction_method": {
+    "type": "zernike_trajectory",
+    "class": "ZernikeTrajectoryExtractor",
+    "method": "extract_trajectory",
+    "reference_angle": 20.0,
+    "focal_length": 22000.0,
+    "inner_radius": 135.75,
+    "description": "Trajectory analysis across 5 elevation angles (20, 30, 40, 50, 60 deg) with OPD correction and annular aperture"
+  },
+  "design_variables": [
+    {
+      "name": "lateral_inner_angle",
+      "expression_name": "lateral_inner_angle",
+      "min": 25.0,
+      "max": 30.0,
+      "baseline": 26.79,
+      "units": "degrees",
+      "enabled": true,
+      "notes": "Inner lateral support angle"
+    },
+    {
+      "name": "lateral_outer_angle",
+      "expression_name": "lateral_outer_angle",
+      "min": 11.0,
+      "max": 17.0,
+      "baseline": 14.64,
+      "units": "degrees",
+      "enabled": true,
+      "notes": "Outer lateral support angle"
+    },
+    {
+      "name": "lateral_outer_pivot",
+      "expression_name": "lateral_outer_pivot",
+      "min": 9.0,
+      "max": 12.0,
+      "baseline": 10.40,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Outer lateral pivot position"
+    },
+    {
+      "name": "lateral_inner_pivot",
+      "expression_name": "lateral_inner_pivot",
+      "min": 5.0,
+      "max": 12.0,
+      "baseline": 10.07,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Inner lateral pivot position"
+    },
+    {
+      "name": "lateral_middle_pivot",
+      "expression_name": "lateral_middle_pivot",
+      "min": 15.0,
+      "max": 27.0,
+      "baseline": 20.73,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Middle lateral pivot position"
+    },
+    {
+      "name": "lateral_closeness",
+      "expression_name": "lateral_closeness",
+      "min": 9.5,
+      "max": 12.5,
+      "baseline": 11.02,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Lateral support closeness parameter"
+    },
+    {
+      "name": "whiffle_min",
+      "expression_name": "whiffle_min",
+      "min": 30.0,
+      "max": 72.0,
+      "baseline": 40.55,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Whiffle tree minimum radius"
+    },
+    {
+      "name": "whiffle_outer_to_vertical",
+      "expression_name": "whiffle_outer_to_vertical",
+      "min": 60.0,
+      "max": 80.0,
+      "baseline": 75.67,
+      "units": "degrees",
+      "enabled": true,
+      "notes": "Whiffle tree outer angle to vertical"
+    },
+    {
+      "name": "whiffle_triangle_closeness",
+      "expression_name": "whiffle_triangle_closeness",
+      "min": 50.0,
+      "max": 80.0,
+      "baseline": 60.00,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Whiffle tree triangle closeness"
+    },
+    {
+      "name": "blank_backface_angle",
+      "expression_name": "blank_backface_angle",
+      "min": 4.1,
+      "max": 4.5,
+      "baseline": 4.15,
+      "units": "degrees",
+      "enabled": true,
+      "notes": "Blank backface angle"
+    },
+    {
+      "name": "inner_circular_rib_dia",
+      "expression_name": "inner_circular_rib_dia",
+      "min": 480.0,
+      "max": 620.0,
+      "baseline": 534.00,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Inner circular rib diameter"
+    }
+  ],
+  "fixed_parameters": [],
+  "constraints": [
+    {
+      "name": "trajectory_fit_quality",
+      "type": "soft",
+      "expression": "linear_fit_r2 >= 0.95",
+      "description": "Linear trajectory model should fit well (R² ≥ 0.95)",
+      "penalty_weight": 100.0
+    },
+    {
+      "name": "blank_mass_max",
+      "type": "hard",
+      "expression": "mass_kg <= 120.0",
+      "description": "Maximum blank mass constraint",
+      "penalty_weight": 1000.0
+    }
+  ],
+  "objectives": [
+    {
+      "name": "total_filtered_rms_nm",
+      "description": "Total integrated RMS across full operating range (20-60 deg)",
+      "direction": "minimize",
+      "weight": 1.0,
+      "target": 4.0,
+      "units": "nm",
+      "notes": "PRIMARY OBJECTIVE - optimized by TPE"
+    },
+    {
+      "name": "coma_rms_nm",
+      "description": "Integrated coma RMS (modes Z7,Z8)",
+      "direction": "minimize",
+      "weight": 0.0,
+      "target": 5.0,
+      "units": "nm",
+      "notes": "LOGGED ONLY - tracks coma contribution"
+    },
+    {
+      "name": "astigmatism_rms_nm",
+      "description": "Integrated astigmatism RMS (modes Z5,Z6)",
+      "direction": "minimize",
+      "weight": 0.0,
+      "target": 5.0,
+      "units": "nm",
+      "notes": "LOGGED ONLY - tracks astigmatism contribution"
+    },
+    {
+      "name": "trefoil_rms_nm",
+      "description": "Integrated trefoil RMS (modes Z9,Z10)",
+      "direction": "minimize",
+      "weight": 0.0,
+      "target": 5.0,
+      "units": "nm",
+      "notes": "LOGGED ONLY - tracks trefoil contribution"
+    },
+    {
+      "name": "spherical_rms_nm",
+      "description": "Integrated spherical RMS (mode Z11)",
+      "direction": "minimize",
+      "weight": 0.0,
+      "target": 8.0,
+      "units": "nm",
+      "notes": "LOGGED ONLY - tracks spherical aberration"
+    },
+    {
+      "name": "linear_fit_r2",
+      "description": "Trajectory model fit quality (should be ~1.0)",
+      "direction": "maximize",
+      "weight": 0.0,
+      "target": 0.95,
+      "units": "unitless",
+      "notes": "LOGGED ONLY - validates physics model"
+    }
+  ],
+  "weighted_sum_formula": "total_filtered_rms_nm (primary) + 0*coma_rms_nm + 0*astigmatism_rms_nm + 0*trefoil_rms_nm + 0*spherical_rms_nm",
+  "zernike_settings": {
+    "n_modes": 50,
+    "filter_low_orders": 4,
+    "displacement_unit": "mm",
+    "subcases": ["1", "2", "5", "3", "6", "4"],
+    "subcase_labels": {
+      "1": "90deg",
+      "2": "20deg",
+      "3": "40deg",
+      "4": "60deg",
+      "5": "30deg",
+      "6": "50deg"
+    },
+    "reference_subcase": "2",
+    "method": "trajectory",
+    "reference_angle": 20.0,
+    "focal_length": 22000.0,
+    "inner_radius": 135.75,
+    "exclude_angles": [90.0]
+  },
+  "nx_settings": {
+    "nx_install_path": "C:\\Program Files\\Siemens\\DesigncenterNX2512",
+    "sim_file": "ASSY_M1_assyfem1_sim1.sim",
+    "solution_name": "Solution 1",
+    "op2_pattern": "*-solution_1.op2",
+    "simulation_timeout_s": 600,
+    "journal_timeout_s": 120,
+    "op2_timeout_s": 1800,
+    "auto_start_nx": true
+  },
+  "dashboard_settings": {
+    "trial_source_tag": true,
+    "fea_marker": "circle",
+    "fea_color": "#4CAF50"
+  }
+}

studies/M1_Mirror/SAT3_Trajectory/README.md

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+# SAT3_Trajectory - Zernike Trajectory Method Optimization
+
+**Status:** Active
+**Created:** 2026-01-29
+**Method:** Zernike Trajectory Analysis
+**Optimizer:** TPE (Tree-Parzen Estimator)
+
+---
+
+## Executive Summary
+
+First production implementation of the **Zernike Trajectory Method** for M1 mirror optimization. Instead of optimizing discrete WFE values at fixed angles, this study optimizes integrated RMS metrics across the full 20°-60° operating range with mode-specific aberration tracking.
+
+**Key Innovation:** Physics-based trajectory model tracks how each Zernike mode (coma, astigmatism, trefoil, spherical) evolves with elevation angle.
+
+---
+
+## Study Overview
+
+### Primary Objective
+- **total_filtered_rms_nm** - Integrated RMS across full operating range (weight=1.0)
+
+### Logged Objectives (Not Optimized, weight=0)
+- **coma_rms_nm** - Coma aberration (Z7, Z8)
+- **astigmatism_rms_nm** - Astigmatism (Z5, Z6)
+- **trefoil_rms_nm** - Trefoil (Z9, Z10)
+- **spherical_rms_nm** - Spherical aberration (Z11)
+- **linear_fit_r2** - Physics model validation (should be ~1.0)
+
+### Design Variables (11 total)
+
+| Parameter | Min | Max | Baseline | Units | Category |
+|-----------|-----|-----|----------|-------|----------|
+| lateral_inner_angle | 25.0 | 30.0 | 26.79 | deg | Lateral Support |
+| lateral_outer_angle | 11.0 | 17.0 | 14.64 | deg | Lateral Support |
+| lateral_outer_pivot | 9.0 | 12.0 | 10.40 | mm | Lateral Support |
+| lateral_inner_pivot | 5.0 | 12.0 | 10.07 | mm | Lateral Support |
+| lateral_middle_pivot | 15.0 | 27.0 | 20.73 | mm | Lateral Support |
+| lateral_closeness | 9.5 | 12.5 | 11.02 | mm | Lateral Support |
+| whiffle_min | 30.0 | 72.0 | 40.55 | mm | Whiffle Tree |
+| whiffle_outer_to_vertical | 60.0 | 80.0 | 75.67 | deg | Whiffle Tree |
+| whiffle_triangle_closeness | 50.0 | 80.0 | 60.00 | mm | Whiffle Tree |
+| blank_backface_angle | 4.1 | 4.5 | 4.15 | deg | Geometry |
+| inner_circular_rib_dia | 480.0 | 620.0 | 534.00 | mm | Geometry |
+
+### Optimizer Configuration
+- **Algorithm:** TPE (Tree-Parzen Estimator)
+- **Budget:** 100 trials
+- **Startup Trials:** 15 (random sampling for initial exploration)
+- **Seed:** 42 (for reproducibility)
+
+### Constraints
+1. **Mass:** blank_mass <= 120 kg (hard constraint, penalty=1e6)
+2. **R² fit:** linear_fit_r2 >= 0.95 (soft constraint, ensures physics model validity)
+
+---
+
+## Trajectory Method Details
+
+### Physics Basis
+
+At elevation angle θ, gravity decomposes into:
+```
+Axial load:   F_axial ∝ sin(θ)
+Lateral load: F_lateral ∝ cos(θ)
+```
+
+Each Zernike coefficient follows:
+```
+c_j(θ) = a_j·(sin θ - sin θ_ref) + b_j·(cos θ - cos θ_ref)
+```
+
+The sensitivity matrix `[a_j, b_j]` reveals which modes respond to axial vs lateral loads.
+
+### Elevation Angles Analyzed
+- **90°** - Manufacturing reference (excluded from trajectory)
+- **20°** - Measurement/polishing reference
+- **30°** - New trajectory point
+- **40°** - Primary operational angle
+- **50°** - New trajectory point
+- **60°** - Secondary operational angle
+
+### Extractor Configuration
+- **Method:** Zernike Trajectory
+- **Reference Angle:** 20° (polishing/measurement)
+- **Focal Length:** 22000 mm (OPD correction for lateral displacements)
+- **Inner Radius:** 135.75 mm (annular aperture, excludes 271.5mm central hole)
+- **N Modes:** 50 (filtered from mode 5 onward)
+
+---
+
+## Expected Performance
+
+### Baseline (from test on M1_Tensor model)
+- **Total Filtered RMS:** 4.30 nm
+- **Coma RMS:** 9.16 nm
+- **Astigmatism RMS:** 6.55 nm
+- **Trefoil RMS:** 6.44 nm
+- **Spherical RMS:** 10.51 nm
+- **R² fit:** 1.0000 (perfect)
+- **Dominant mode:** Spherical
+
+### Optimization Targets
+- **Total Filtered RMS:** < 4.0 nm
+- **Coma RMS:** < 5.0 nm
+- **R² fit:** > 0.95 (validates physics model)
+
+---
+
+## Usage
+
+### Start Optimization
+```bash
+cd studies/M1_Mirror/SAT3_Trajectory
+python run_optimization.py --start
+```
+
+### Resume Optimization
+```bash
+python run_optimization.py --start --resume
+```
+
+### Custom Trial Count
+```bash
+python run_optimization.py --start --trials 150
+```
+
+### Test Single FEA Run
+```bash
+python run_optimization.py --test
+```
+
+### Analyze Results
+```bash
+# View convergence
+python -m optimization_engine.reporting.visualizer 3_results/study.db
+
+# Generate report
+python -m optimization_engine.reporting.markdown_report 3_results/study.db
+```
+
+---
+
+## File Structure
+
+```
+SAT3_Trajectory/
+├── README.md                    (This file)
+├── STUDY_REPORT.md              (Results report - updated after optimization)
+├── run_optimization.py          (Main optimization script)
+├── 1_setup/
+│   ├── optimization_config.json (Study configuration)
+│   └── model/                   (NX model files - copied from M1_Tensor)
+│       ├── ASSY_M1.prt
+│       ├── ASSY_M1_assyfem1.afm
+│       ├── ASSY_M1_assyfem1_sim1.sim
+│       ├── M1_Blank.prt
+│       ├── M1_Blank_fem1.fem
+│       ├── M1_Blank_fem1_i.prt
+│       ├── M1_Vertical_Support_Skeleton.prt
+│       ├── M1_Vertical_Support_Skeleton_fem1.fem
+│       └── M1_Vertical_Support_Skeleton_fem1_i.prt
+├── 2_iterations/
+│   ├── iter_0001/               (Trial 1 FEA files)
+│   ├── iter_0002/               (Trial 2 FEA files)
+│   └── ...
+└── 3_results/
+    ├── study.db                 (Optuna database)
+    ├── optimization.log         (Execution log)
+    └── trajectory_analysis/     (Mode-specific analysis plots)
+```
+
+---
+
+## Key Differences from Previous Studies
+
+### vs. Discrete WFE Optimization (V11-V15)
+- **Old:** Optimize `6*wfe_40_20 + 5*wfe_60_20 + 3*mfg_90`
+- **New:** Optimize integrated RMS across continuous operating range
+- **Benefit:** Physics-based, mode-specific tracking, better understanding of support behavior
+
+### vs. SAT (Surrogate-Assisted Tuning)
+- **SAT:** Builds neural surrogate for fast exploration (100 FEA + 10K surrogate)
+- **TPE:** Direct Bayesian optimization (100 FEA, no surrogate)
+- **This Study:** TPE for initial trajectory exploration, may switch to SAT later
+
+---
+
+## Dependencies
+
+- **NX 2512** (FEA solver)
+- **Python 3.9+** (Atomizer environment)
+- **Optuna** (TPE sampler)
+- **pyNastran** (OP2 reading)
+- **NumPy** (trajectory fitting)
+
+---
+
+## References
+
+- **Physics Documentation:** `docs/physics/ZERNIKE_TRAJECTORY_METHOD.md`
+- **Implementation:** `optimization_engine/extractors/extract_zernike_trajectory.py`
+- **Example Config:** `docs/examples/trajectory_optimization_config.yaml`
+- **Handoff Doc:** `docs/handoff/SETUP_TRAJECTORY_OPTIMIZATION.md`
+
+---
+
+## Notes
+
+1. **R² Monitoring:** If R² drops below 0.95, it indicates nonlinearity (e.g., contact lifting). Designs with poor R² should be investigated.
+
+2. **Mode-Specific Insights:** After optimization, analyze which modes improved most. Coma improvements indicate lateral support changes were effective.
+
+3. **Comparison with V15:** After completion, compare trajectory-based results with V15 NSGA-II Pareto front to validate new method.
+
+4. **Future Work:** If this study succeeds, extend to SAT with trajectory objectives for 10K+ design exploration.
+
+---
+
+*Study created by Atomizer, 2026-01-29*
+*First implementation of Zernike Trajectory Method for M1 GigaBIT mirror*

studies/M1_Mirror/SAT3_Trajectory/STUDY_REPORT.md

@@ -0,0 +1,168 @@
+# SAT3_Trajectory - Study Report
+
+**Status:** _pending optimization_
+**Optimization Started:** _pending_
+**Optimization Completed:** _pending_
+**Total Trials:** _pending_
+
+---
+
+## Optimization Summary
+
+| Metric | Value |
+|--------|-------|
+| Algorithm | TPE (Tree-Parzen Estimator) |
+| Design Variables | 11 |
+| Total Trials | _pending_ |
+| Successful FEA | _pending_ |
+| Failed FEA | _pending_ |
+| Best Trial Number | _pending_ |
+| Best Weighted Sum | _pending_ nm |
+
+---
+
+## Best Design
+
+### Objectives
+
+| Objective | Best Value | Baseline | Improvement |
+|-----------|------------|----------|-------------|
+| **total_filtered_rms_nm** (PRIMARY) | _pending_ nm | 4.30 nm | _pending_ % |
+| coma_rms_nm (logged) | _pending_ nm | 9.16 nm | _pending_ % |
+| astigmatism_rms_nm (logged) | _pending_ nm | 6.55 nm | _pending_ % |
+| trefoil_rms_nm (logged) | _pending_ nm | 6.44 nm | _pending_ % |
+| spherical_rms_nm (logged) | _pending_ nm | 10.51 nm | _pending_ % |
+| linear_fit_r2 | _pending_ | 1.0000 | _pending_ |
+| mass_kg | _pending_ kg | _pending_ kg | _pending_ % |
+
+### Design Parameters
+
+| Parameter | Best Value | Baseline | Delta |
+|-----------|------------|----------|-------|
+| lateral_inner_angle | _pending_ deg | 26.79 deg | _pending_ |
+| lateral_outer_angle | _pending_ deg | 14.64 deg | _pending_ |
+| lateral_outer_pivot | _pending_ mm | 10.40 mm | _pending_ |
+| lateral_inner_pivot | _pending_ mm | 10.07 mm | _pending_ |
+| lateral_middle_pivot | _pending_ mm | 20.73 mm | _pending_ |
+| lateral_closeness | _pending_ mm | 11.02 mm | _pending_ |
+| whiffle_min | _pending_ mm | 40.55 mm | _pending_ |
+| whiffle_outer_to_vertical | _pending_ deg | 75.67 deg | _pending_ |
+| whiffle_triangle_closeness | _pending_ mm | 60.00 mm | _pending_ |
+| blank_backface_angle | _pending_ deg | 4.15 deg | _pending_ |
+| inner_circular_rib_dia | _pending_ mm | 534.00 mm | _pending_ |
+
+---
+
+## Mode-Specific Analysis
+
+### Which Modes Improved Most?
+
+_Analysis pending - shows which aberration types benefited from optimization_
+
+### Sensitivity Matrix
+
+_Analysis pending - shows which modes respond to axial vs lateral loads_
+
+### R² Validation
+
+_Analysis pending - confirms physics model held throughout optimization_
+
+---
+
+## Convergence Analysis
+
+### Total Filtered RMS vs Trial
+
+_Plot pending_
+
+### Mode-Specific RMS vs Trial
+
+_Plot pending - overlay coma, astigmatism, trefoil, spherical_
+
+### Parameter Evolution
+
+_Plot pending - shows how design variables evolved_
+
+---
+
+## Key Findings
+
+### 1. Trajectory Method Validation
+
+_Analysis pending_
+
+- Did R² stay > 0.95 throughout?
+- Were any designs nonlinear?
+- Did the physics model hold?
+
+### 2. Mode-Specific Insights
+
+_Analysis pending_
+
+- Which modes improved most?
+- Which modes are dominant now?
+- Does coma reduction correlate with lateral support changes?
+
+### 3. Comparison with V15 NSGA-II
+
+_Analysis pending_
+
+- How does best trajectory result compare to V15 Pareto front?
+- Is trajectory method competitive?
+- What insights does trajectory provide that discrete WFE doesn't?
+
+### 4. Lateral vs Axial Sensitivity
+
+_Analysis pending_
+
+- Which parameters affect which modes?
+- Are lateral supports primarily controlling coma (as predicted)?
+- Are axial supports (whiffle tree) controlling spherical?
+
+---
+
+## Recommendations
+
+### Next Steps
+
+_Analysis pending - based on results, suggest:_
+
+1. Whether to proceed with SAT trajectory optimization
+2. Which modes need further attention
+3. Whether to refine parameter bounds
+4. Additional angles to include in trajectory
+
+### Parameter Bounds Refinement
+
+_Analysis pending_
+
+- Did any parameters hit bounds?
+- Should ranges be expanded or narrowed?
+
+### Future Studies
+
+_Analysis pending_
+
+- SAT3_Trajectory_SAT (100 FEA + 10K surrogate)
+- Multi-objective trajectory (optimize modes separately)
+- Trajectory + mass trade-off
+
+---
+
+## Files Generated
+
+- `3_results/study.db` - Optuna database with all trials
+- `3_results/optimization.log` - Full execution log
+- `2_iterations/iter_XXXX/` - FEA results for each trial
+- _(Trajectory analysis plots - TBD)_
+
+---
+
+## Lessons Learned
+
+_To be filled after optimization completes_
+
+---
+
+*Report template created: 2026-01-29*
+*To be updated after optimization completes*

studies/M1_Mirror/SAT3_Trajectory/run_optimization.py

@@ -0,0 +1,485 @@
+#!/usr/bin/env python3
+"""
+M1 Mirror SAT3_Trajectory - Trajectory-Based Optimization (TPE)
+================================================================
+
+First implementation of Zernike Trajectory Method for M1 mirror optimization.
+
+Key Features:
+1. TPE sampler - 100 trials, 15 startup
+2. Trajectory analysis across 5 angles (20°, 30°, 40°, 50°, 60°)
+3. Primary objective: total_filtered_rms_nm (integrated RMS across operating range)
+4. Logged objectives (weight=0): coma_rms_nm, astigmatism_rms_nm, trefoil_rms_nm, spherical_rms_nm
+5. Full wiffle tree + lateral support parameters (11 design variables)
+6. OPD correction with focal_length=22000mm and annular aperture (inner_radius=135.75mm)
+
+Usage:
+    python run_optimization.py --start
+    python run_optimization.py --start --trials 100
+    python run_optimization.py --start --trials 100 --resume
+    python run_optimization.py --test  # Single trial test
+
+Author: Atomizer
+Created: 2026-01-29
+"""
+
+import sys
+import os
+import subprocess
+
+LICENSE_SERVER = "28000@dalidou;28000@100.80.199.40"
+os.environ['SPLM_LICENSE_SERVER'] = LICENSE_SERVER
+print(f"[LICENSE] SPLM_LICENSE_SERVER set to: {LICENSE_SERVER}")
+
+# Add Atomizer root to path
+STUDY_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(STUDY_DIR)))
+sys.path.insert(0, PROJECT_ROOT)
+
+import json
+import time
+import argparse
+import logging
+import shutil
+import re
+from pathlib import Path
+from typing import Dict, Optional, Any
+from datetime import datetime
+
+
+# ============================================================================
+# Dashboard Auto-Launch
+# ============================================================================
+
+def launch_dashboard():
+    """Launch the Atomizer dashboard in background."""
+    dashboard_dir = Path(PROJECT_ROOT) / "atomizer-dashboard"
+    start_script = dashboard_dir / "start-dashboard.bat"
+
+    if not start_script.exists():
+        print(f"[DASHBOARD] Warning: start-dashboard.bat not found at {start_script}")
+        return False
+
+    try:
+        subprocess.Popen(
+            ["cmd", "/c", "start", "/min", str(start_script)],
+            cwd=str(dashboard_dir),
+            shell=True,
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL
+        )
+        print("[DASHBOARD] Launched in background")
+        print("[DASHBOARD] Frontend: http://localhost:5173")
+        print("[DASHBOARD] Backend:  http://localhost:8000")
+        return True
+    except Exception as e:
+        print(f"[DASHBOARD] Failed to launch: {e}")
+        return False
+
+import optuna
+from optuna.samplers import TPESampler
+
+# Atomizer imports
+from optimization_engine.nx.solver import NXSolver
+from optimization_engine.extractors.extract_zernike_trajectory import extract_zernike_trajectory
+
+# ============================================================================
+# Paths
+# ============================================================================
+
+STUDY_DIR = Path(__file__).parent
+SETUP_DIR = STUDY_DIR / "1_setup"
+MODEL_DIR = SETUP_DIR / "model"
+ITERATIONS_DIR = STUDY_DIR / "2_iterations"
+RESULTS_DIR = STUDY_DIR / "3_results"
+CONFIG_PATH = SETUP_DIR / "optimization_config.json"
+
+# Ensure directories exist
+ITERATIONS_DIR.mkdir(exist_ok=True)
+RESULTS_DIR.mkdir(exist_ok=True)
+
+# Logging
+LOG_FILE = RESULTS_DIR / "optimization.log"
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s | %(levelname)-8s | %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler(LOG_FILE, mode='a')
+    ]
+)
+logger = logging.getLogger(__name__)
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+with open(CONFIG_PATH) as f:
+    CONFIG = json.load(f)
+
+STUDY_NAME = CONFIG["study_name"]
+
+# Primary objective is total_filtered_rms_nm (weight=1.0)
+# All other objectives are logged only (weight=0)
+def compute_weighted_sum(objectives: Dict[str, float]) -> float:
+    """Compute weighted sum - only total_filtered_rms_nm has weight=1.0."""
+    return objectives.get('total_filtered_rms_nm', 1000.0)
+
+
+# Hard constraint: blank_mass <= 120kg
+MAX_BLANK_MASS_KG = 120.0
+CONSTRAINT_PENALTY = 1e6
+
+# Trajectory settings
+REFERENCE_ANGLE = CONFIG['extraction_method'].get('reference_angle', 20.0)
+FOCAL_LENGTH = CONFIG['extraction_method'].get('focal_length', 22000.0)
+INNER_RADIUS_MM = CONFIG['extraction_method'].get('inner_radius', 135.75)
+
+
+def check_mass_constraint(mass_kg: float) -> tuple:
+    """Check if mass constraint is satisfied."""
+    if mass_kg <= MAX_BLANK_MASS_KG:
+        return True, 0.0
+    else:
+        return False, mass_kg - MAX_BLANK_MASS_KG
+
+
+# ============================================================================
+# FEA Runner with Trajectory Extraction
+# ============================================================================
+
+class FEARunner:
+    """Runs FEA simulations with Zernike Trajectory extraction."""
+
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.nx_solver = None
+        self.master_model_dir = MODEL_DIR
+
+        # Get fixed parameter values
+        self.fixed_params = {}
+        for fp in config.get('fixed_parameters', []):
+            self.fixed_params[fp['name']] = fp['value']
+
+    def setup(self):
+        """Setup NX solver (assumes NX is already running)."""
+        study_name = self.config.get('study_name', 'SAT3_Trajectory')
+
+        nx_settings = self.config.get('nx_settings', {})
+        nx_install_dir = nx_settings.get('nx_install_path', 'C:\\Program Files\\Siemens\\DesigncenterNX2512')
+        version_match = re.search(r'NX(\d+)|DesigncenterNX(\d+)', nx_install_dir)
+        nastran_version = (version_match.group(1) or version_match.group(2)) if version_match else "2512"
+
+        self.nx_solver = NXSolver(
+            master_model_dir=str(self.master_model_dir),
+            nx_install_dir=nx_install_dir,
+            nastran_version=nastran_version,
+            timeout=nx_settings.get('simulation_timeout_s', 600),
+            use_iteration_folders=True,
+            study_name=study_name
+        )
+        logger.info(f"[NX] Solver ready (Nastran {nastran_version})")
+
+    def run_fea(self, params: Dict[str, float], trial_num: int) -> Optional[Dict]:
+        """Run FEA and extract objectives using Zernike Trajectory Method."""
+        if self.nx_solver is None:
+            self.setup()
+
+        logger.info(f"  [FEA {trial_num}] Running simulation...")
+
+        # Build expressions
+        expressions = {}
+        for name, value in self.fixed_params.items():
+            expressions[name] = value
+        for var in self.config['design_variables']:
+            if var.get('enabled', True) and var['name'] in params:
+                expressions[var['expression_name']] = params[var['name']]
+
+        iter_folder = self.nx_solver.create_iteration_folder(
+            iterations_base_dir=ITERATIONS_DIR,
+            iteration_number=trial_num,
+            expression_updates=expressions
+        )
+
+        try:
+            nx_settings = self.config.get('nx_settings', {})
+            sim_file = iter_folder / nx_settings.get('sim_file', 'ASSY_M1_assyfem1_sim1.sim')
+
+            t_start = time.time()
+
+            result = self.nx_solver.run_simulation(
+                sim_file=sim_file,
+                working_dir=iter_folder,
+                expression_updates=expressions,
+                solution_name=nx_settings.get('solution_name', 'Solution 1'),
+                cleanup=False
+            )
+
+            solve_time = time.time() - t_start
+
+            if not result['success']:
+                logger.error(f"  [FEA {trial_num}] Solve failed: {result.get('error')}")
+                return None
+
+            logger.info(f"  [FEA {trial_num}] Solved in {solve_time:.1f}s")
+
+            # Extract objectives using Zernike Trajectory Method
+            op2_path = Path(result['op2_file'])
+            objectives = self._extract_objectives_trajectory(op2_path, iter_folder)
+
+            if objectives is None:
+                return None
+
+            # Check constraint
+            mass_kg = objectives['mass_kg']
+            is_feasible, violation = check_mass_constraint(mass_kg)
+
+            if is_feasible:
+                weighted_sum = compute_weighted_sum(objectives)
+                constraint_status = "OK"
+            else:
+                weighted_sum = compute_weighted_sum(objectives) + CONSTRAINT_PENALTY * violation
+                constraint_status = f"VIOLATED (+{violation:.1f}kg)"
+
+            logger.info(f"  [FEA {trial_num}] Total RMS:   {objectives['total_filtered_rms_nm']:.2f} nm  (PRIMARY)")
+            logger.info(f"  [FEA {trial_num}] Coma RMS:    {objectives['coma_rms_nm']:.2f} nm  (logged)")
+            logger.info(f"  [FEA {trial_num}] Astig RMS:   {objectives['astigmatism_rms_nm']:.2f} nm  (logged)")
+            logger.info(f"  [FEA {trial_num}] Trefoil RMS: {objectives['trefoil_rms_nm']:.2f} nm  (logged)")
+            logger.info(f"  [FEA {trial_num}] Spher RMS:   {objectives['spherical_rms_nm']:.2f} nm  (logged)")
+            logger.info(f"  [FEA {trial_num}] R² fit:      {objectives['linear_fit_r2']:.4f}  (physics validation)")
+            logger.info(f"  [FEA {trial_num}] Mass:        {objectives['mass_kg']:.3f} kg  [Constraint: {constraint_status}]")
+            logger.info(f"  [FEA {trial_num}] Weighted Sum: {weighted_sum:.2f}")
+
+            return {
+                'trial_num': trial_num,
+                'params': params,
+                'objectives': objectives,
+                'weighted_sum': weighted_sum,
+                'is_feasible': is_feasible,
+                'constraint_violation': violation,
+                'source': 'FEA_ZernikeTrajectory',
+                'solve_time': solve_time,
+                'iter_folder': str(iter_folder)
+            }
+
+        except Exception as e:
+            logger.error(f"  [FEA {trial_num}] Error: {e}")
+            import traceback
+            traceback.print_exc()
+            return None
+
+    def _extract_objectives_trajectory(self, op2_path: Path, iter_folder: Path) -> Optional[Dict]:
+        """
+        Extract objectives using Zernike Trajectory Method.
+
+        Analyzes 5 elevation angles (20, 30, 40, 50, 60 deg) and provides:
+        - total_filtered_rms_nm: Integrated RMS across operating range
+        - Mode-specific RMS: coma, astigmatism, trefoil, spherical
+        - linear_fit_r2: Physics model validation
+        """
+        try:
+            # Run trajectory extraction
+            result = extract_zernike_trajectory(
+                op2_file=op2_path,
+                reference_angle=REFERENCE_ANGLE,
+                focal_length=FOCAL_LENGTH,
+                unit='mm'
+            )
+
+            # Extract mass from temp file
+            mass_kg = 0.0
+            mass_file = iter_folder / "_temp_mass.txt"
+            if mass_file.exists():
+                try:
+                    with open(mass_file, 'r') as f:
+                        mass_kg = float(f.read().strip())
+                except Exception as mass_err:
+                    logger.warning(f"  Could not read mass file: {mass_err}")
+
+            if mass_kg == 0:
+                props_file = iter_folder / "_temp_part_properties.json"
+                if props_file.exists():
+                    try:
+                        with open(props_file, 'r') as f:
+                            props = json.load(f)
+                            mass_kg = props.get('mass_kg', 0)
+                    except Exception:
+                        pass
+
+            objectives = {
+                'total_filtered_rms_nm': result['total_filtered_rms_nm'],
+                'coma_rms_nm': result['coma_rms_nm'],
+                'astigmatism_rms_nm': result['astigmatism_rms_nm'],
+                'trefoil_rms_nm': result['trefoil_rms_nm'],
+                'spherical_rms_nm': result['spherical_rms_nm'],
+                'linear_fit_r2': result['linear_fit_r2'],
+                'mass_kg': mass_kg
+            }
+
+            return objectives
+
+        except Exception as e:
+            logger.error(f"Trajectory extraction failed: {e}")
+            import traceback
+            traceback.print_exc()
+            return None
+
+
+# ============================================================================
+# TPE Optimizer
+# ============================================================================
+
+class TPEOptimizer:
+    """TPE optimizer for trajectory-based optimization."""
+
+    def __init__(self, config: Dict[str, Any], resume: bool = False):
+        self.config = config
+        self.resume = resume
+        self.fea_runner = FEARunner(config)
+
+        # Load design variable bounds
+        self.design_vars = {
+            v['name']: {'min': v['min'], 'max': v['max'], 'baseline': v.get('baseline')}
+            for v in config['design_variables']
+            if v.get('enabled', True)
+        }
+
+        # TPE settings
+        opt_settings = config.get('optimization', {})
+        self.n_startup_trials = opt_settings.get('n_startup_trials', 15)
+        self.seed = opt_settings.get('seed', 42)
+
+        # Study
+        self.study_name = config.get('study_name', 'SAT3_Trajectory')
+        self.db_path = RESULTS_DIR / "study.db"
+
+        # Track best
+        self.best_weighted_sum = float('inf')
+        self.best_trial_info = None
+
+        # Track FEA count
+        self._count_existing_iterations()
+
+    def _count_existing_iterations(self):
+        """Count existing iteration folders."""
+        self.fea_count = 0
+        if ITERATIONS_DIR.exists():
+            for d in ITERATIONS_DIR.iterdir():
+                if d.is_dir() and d.name.startswith('iter'):
+                    self.fea_count += 1
+        logger.info(f"[INIT] Found {self.fea_count} existing FEA runs")
+
+    def objective_function(self, trial: optuna.Trial) -> float:
+        """Optuna objective function."""
+        # Sample parameters
+        params = {}
+        for name, bounds in self.design_vars.items():
+            params[name] = trial.suggest_float(name, bounds['min'], bounds['max'])
+
+        # Run FEA
+        self.fea_count += 1
+        result = self.fea_runner.run_fea(params, self.fea_count)
+
+        if result is None:
+            raise optuna.TrialPruned()
+
+        # Log all objectives (TPE only optimizes the return value, but we want all logged)
+        for obj_name, obj_value in result['objectives'].items():
+            trial.set_user_attr(obj_name, obj_value)
+
+        trial.set_user_attr('is_feasible', result['is_feasible'])
+        trial.set_user_attr('solve_time', result['solve_time'])
+        trial.set_user_attr('source', result['source'])
+
+        # Track best
+        if result['weighted_sum'] < self.best_weighted_sum:
+            self.best_weighted_sum = result['weighted_sum']
+            self.best_trial_info = result
+            logger.info(f"  [NEW BEST] Trial {result['trial_num']}: {result['weighted_sum']:.2f}")
+
+        return result['weighted_sum']
+
+    def run(self, n_trials: int = 100):
+        """Run TPE optimization."""
+        logger.info("="*80)
+        logger.info(f"Starting TPE Trajectory Optimization: {self.study_name}")
+        logger.info(f"Design Variables: {len(self.design_vars)}")
+        logger.info(f"n_trials: {n_trials}, n_startup_trials: {self.n_startup_trials}")
+        logger.info("="*80)
+
+        # Create or load study
+        storage = f"sqlite:///{self.db_path}"
+        if self.resume:
+            logger.info(f"[RESUME] Loading existing study from {self.db_path}")
+            study = optuna.load_study(study_name=self.study_name, storage=storage)
+        else:
+            logger.info(f"[NEW] Creating new study at {self.db_path}")
+            sampler = TPESampler(
+                n_startup_trials=self.n_startup_trials,
+                seed=self.seed
+            )
+            study = optuna.create_study(
+                study_name=self.study_name,
+                storage=storage,
+                sampler=sampler,
+                direction='minimize',
+                load_if_exists=False
+            )
+
+        # Run optimization
+        study.optimize(self.objective_function, n_trials=n_trials)
+
+        logger.info("="*80)
+        logger.info(f"Optimization Complete!")
+        logger.info(f"Best weighted sum: {self.best_weighted_sum:.2f}")
+        if self.best_trial_info:
+            logger.info(f"Best objectives:")
+            for k, v in self.best_trial_info['objectives'].items():
+                logger.info(f"  {k}: {v:.3f}")
+        logger.info("="*80)
+
+
+# ============================================================================
+# Main Entry Point
+# ============================================================================
+
+def main():
+    parser = argparse.ArgumentParser(description="SAT3 Trajectory Optimization (TPE)")
+    parser.add_argument('--start', action='store_true', help='Start optimization')
+    parser.add_argument('--trials', type=int, default=100, help='Number of trials (default: 100)')
+    parser.add_argument('--resume', action='store_true', help='Resume existing study')
+    parser.add_argument('--test', action='store_true', help='Run single FEA test')
+    parser.add_argument('--no-dashboard', action='store_true', help="Don't auto-launch dashboard")
+
+    args = parser.parse_args()
+
+    if args.start:
+        # Launch dashboard unless disabled
+        if not args.no_dashboard:
+            launch_dashboard()
+            time.sleep(2)
+
+        optimizer = TPEOptimizer(CONFIG, resume=args.resume)
+        optimizer.run(n_trials=args.trials)
+
+    elif args.test:
+        logger.info("[TEST] Running single FEA trial...")
+        runner = FEARunner(CONFIG)
+
+        # Use baseline values
+        params = {v['name']: v['baseline'] for v in CONFIG['design_variables'] if v.get('enabled', True)}
+
+        result = runner.run_fea(params, trial_num=0)
+        if result:
+            logger.info("[TEST] Success!")
+        else:
+            logger.error("[TEST] Failed")
+            sys.exit(1)
+
+    else:
+        parser.print_help()
+
+
+if __name__ == '__main__':
+    main()

test_trajectory_extractor.py

@@ -0,0 +1,48 @@
+"""Test the Zernike Trajectory extractor on an existing OP2 file."""
+
+from optimization_engine.extractors.extract_zernike_trajectory import extract_zernike_trajectory
+from pathlib import Path
+
+op2_file = Path(r'tests\M1_Tensor\Atomizer_M1_Best_2026-01-29 - Tensor\assy_m1_assyfem1_sim1-solution_1.op2')
+
+print(f'Testing trajectory extractor on: {op2_file}')
+print('=' * 60)
+
+try:
+    result = extract_zernike_trajectory(
+        op2_file,
+        reference_angle=20.0,
+        focal_length=22000.0
+    )
+
+    print('[OK] Extractor ran successfully!')
+    print()
+    print(f'Angles detected: {result["angles_deg"]}')
+    print(f'Reference angle: {result["reference_angle"]} deg')
+    print(f'Number of angles: {result["n_angles"]}')
+    print()
+    print(f'Linear fit R2: {result["linear_fit_r2"]:.4f}')
+    if result["linear_fit_r2"] > 0.95:
+        print('  [OK] Excellent fit - physics model validated')
+    elif result["linear_fit_r2"] > 0.85:
+        print('  [~] Good fit - some nonlinearity present')
+    else:
+        print('  [!] Poor fit - significant nonlinearity')
+    print()
+    print('--- Mode-Specific RMS (nm) ---')
+    print(f'Total Filtered RMS: {result["total_filtered_rms_nm"]:.2f} nm')
+    print(f'Coma RMS:           {result["coma_rms_nm"]:.2f} nm')
+    print(f'Astigmatism RMS:    {result["astigmatism_rms_nm"]:.2f} nm')
+    print(f'Trefoil RMS:        {result["trefoil_rms_nm"]:.2f} nm')
+    print(f'Spherical RMS:      {result["spherical_rms_nm"]:.2f} nm')
+    print()
+    print(f'Dominant mode: {result["dominant_mode"]}')
+    print(f'Mode ranking: {", ".join(result["mode_ranking"][:5])}')
+    print()
+    print('[OK] All validation checks passed!')
+
+except Exception as e:
+    print(f'[ERROR] {e}')
+    import traceback
+    traceback.print_exc()
+    exit(1)