fix(canvas): Multiple fixes for drag-drop, undo/redo, and code generation

Drag-drop fixes: - Fix Objective default data: use nested 'source' object with extractor_id/output_name - Fix Constraint default data: use 'type' field (not constraint_type), 'threshold' (not limit) Undo/Redo fixes: - Remove dependency on isDirty flag (which is always false due to auto-save) - Record snapshots based on actual spec changes via deep comparison Code generation improvements: - Update system prompt to support multiple extractor types: * OP2-based extractors for FEA results (stress, displacement, frequency) * Expression-based extractors for NX model values (dimensions, volumes) * Computed extractors for derived values (no FEA needed) - Claude will now choose appropriate signature based on user's description
2026-01-20 15:08:49 -05:00
parent 89694088a2
commit 5c419e2358
30 changed files with 1781 additions and 85 deletions
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/ASSY_M1.prt
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/ASSY_M1.prt
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/ASSY_M1_assyfem1.afm
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/ASSY_M1_assyfem1.afm
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/ASSY_M1_assyfem1_sim1.sim
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/ASSY_M1_assyfem1_sim1.sim
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank.prt
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank.prt
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank_fem1.fem
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank_fem1.fem
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank_fem1_i.prt
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank_fem1_i.prt
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Vertical_Support_Skeleton_fem1.fem
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Vertical_Support_Skeleton_fem1.fem
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/_introspection_cache.json
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/_introspection_cache.json
@@ -0,0 +1,195 @@
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--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/_temp_introspection.json
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/_temp_introspection.json
@@ -0,0 +1,195 @@
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--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model_expressions.json
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model_expressions.json
@@ -0,0 +1,58 @@
+{
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+  "whiffle_p1": 390.0,
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+  "whiffle_p3": 80.0,
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+  "rib_thickness_lataral": 10.0,
+  "hole_count": 10.0,
+  "Pocket_Radius": 10.05,
+  "lateral_closeness": 7.89,
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+  "inner_circular_rib_dia": 537.86,
+  "whiffle_min": 56.65,
+  "Pattern_p5610": 60.0,
+  "whiffle_triangle_closeness": 69.24,
+  "beam_face_thickness": 20.0,
+  "offset_lateral_support_contact": 5.0,
+  "Pattern_p2656": 360.0,
+  "rib_thickness_lateral_truss": 12.06,
+  "whiffle_max": 8.0,
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+  "Pattern_p2653": 3.0,
+  "Pattern_p2654": 120.0,
+  "Pattern_p2883": 3.0,
+  "Pattern_p2884": 120.0,
+  "Pattern_p2886": 360.0,
+  "ribs_circular_thk": 6.81,
+  "support_cone_angle": 0.0,
+  "rib_thickness": 8.07,
+  "rib_lin_1": 60.0,
+  "rib_lin_2": 80.0,
+  "rib_lin_4": 80.0,
+  "in_between_u": 0.5,
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+  "lateral_middle_pivot": 21.07,
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+  "lateral_inner_u": 0.3,
+  "center_thickness": 85.0,
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+  "lateral_outer_pivot": 8.615999999999998,
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+  "Pattern_p3830": 120.0,
+  "Pattern_p3832": 360.0,
+  "p1050_y": 0.0,
+  "holes_diameter": 400.0,
+  "lateral_outer_angle": 10.77,
+  "vertical_support_diameter_seat_offset": 10.0,
+  "lateral_outer_u": 0.8,
+  "vertical_support_seat_depth": 20.0,
+  "lateral_inner_pivot": 9.578999999999997
+}
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/optimization_config.json
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/optimization_config.json
@@ -0,0 +1,131 @@
+{
+  "$schema": "Atomizer M1 Mirror Cost Reduction - Lateral Supports Optimization",
+  "study_name": "m1_mirror_cost_reduction_lateral",
+  "study_tag": "CMA-ES-100",
+  "description": "Lateral support optimization with new U-joint expressions (lateral_inner_u, lateral_outer_u) for cost reduction model. Focus on WFE and MFG only - no mass objective.",
+  "business_context": {
+    "purpose": "Optimize lateral support geometry using new U-joint parameterization on cost reduction model",
+    "benefit": "Improved lateral support performance with cleaner parameterization",
+    "goal": "Minimize WFE at 40/60 deg and MFG at 90 deg"
+  },
+  "optimization": {
+    "algorithm": "CMA-ES",
+    "n_trials": 100,
+    "n_startup_trials": 0,
+    "sigma0": 0.3,
+    "notes": "CMA-ES is optimal for 5D continuous optimization - fast convergence, robust"
+  },
+  "extraction_method": {
+    "type": "zernike_opd",
+    "class": "ZernikeOPDExtractor",
+    "method": "extract_relative",
+    "inner_radius": 135.75,
+    "description": "OPD-based Zernike with ANNULAR aperture (271.5mm central hole excluded)"
+  },
+  "design_variables": [
+    {
+      "name": "lateral_inner_u",
+      "expression_name": "lateral_inner_u",
+      "min": 0.2,
+      "max": 0.95,
+      "baseline": 0.3,
+      "units": "unitless",
+      "enabled": true,
+      "notes": "U-joint ratio for inner lateral support (replaces lateral_inner_pivot)"
+    },
+    {
+      "name": "lateral_outer_u",
+      "expression_name": "lateral_outer_u",
+      "min": 0.2,
+      "max": 0.95,
+      "baseline": 0.8,
+      "units": "unitless",
+      "enabled": true,
+      "notes": "U-joint ratio for outer lateral support (replaces lateral_outer_pivot)"
+    },
+    {
+      "name": "lateral_middle_pivot",
+      "expression_name": "lateral_middle_pivot",
+      "min": 15.0,
+      "max": 27.0,
+      "baseline": 21.07,
+      "units": "mm",
+      "enabled": true,
+      "notes": "Middle pivot position on lateral support"
+    },
+    {
+      "name": "lateral_inner_angle",
+      "expression_name": "lateral_inner_angle",
+      "min": 25.0,
+      "max": 35.0,
+      "baseline": 31.93,
+      "units": "degrees",
+      "enabled": true,
+      "notes": "Inner lateral support angle"
+    },
+    {
+      "name": "lateral_outer_angle",
+      "expression_name": "lateral_outer_angle",
+      "min": 8.0,
+      "max": 17.0,
+      "baseline": 10.77,
+      "units": "degrees",
+      "enabled": true,
+      "notes": "Outer lateral support angle"
+    }
+  ],
+  "fixed_parameters": [],
+  "constraints": [
+    {
+      "name": "blank_mass_max",
+      "type": "hard",
+      "expression": "mass_kg <= 120.0",
+      "description": "Maximum blank mass constraint (still enforced even though mass not optimized)",
+      "penalty_weight": 1000.0
+    }
+  ],
+  "objectives": [
+    {
+      "name": "wfe_40_20",
+      "description": "Filtered RMS WFE at 40 deg relative to 20 deg (annular)",
+      "direction": "minimize",
+      "weight": 6.0,
+      "target": 4.0,
+      "units": "nm"
+    },
+    {
+      "name": "wfe_60_20",
+      "description": "Filtered RMS WFE at 60 deg relative to 20 deg (annular)",
+      "direction": "minimize",
+      "weight": 5.0,
+      "target": 10.0,
+      "units": "nm"
+    },
+    {
+      "name": "mfg_90",
+      "description": "Manufacturing deformation at 90 deg polishing (J1-J3 filtered, annular)",
+      "direction": "minimize",
+      "weight": 3.0,
+      "target": 20.0,
+      "units": "nm"
+    }
+  ],
+  "weighted_sum_formula": "6*wfe_40_20 + 5*wfe_60_20 + 3*mfg_90",
+  "zernike_settings": {
+    "n_modes": 50,
+    "filter_low_orders": 4,
+    "displacement_unit": "mm",
+    "subcases": ["1", "2", "3", "4"],
+    "subcase_labels": {"1": "90deg", "2": "20deg", "3": "40deg", "4": "60deg"},
+    "reference_subcase": "2",
+    "method": "opd",
+    "inner_radius": 135.75
+  },
+  "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
+  }
+}
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/3_results/optimization_summary.json
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/3_results/optimization_summary.json
@@ -0,0 +1,28 @@
+{
+  "study_name": "m1_mirror_cost_reduction_lateral",
+  "algorithm": "CMA-ES",
+  "extraction_method": "ZernikeOPD_Annular",
+  "inner_radius_mm": 135.75,
+  "objectives_note": "Mass NOT in objective - WFE only",
+  "total_trials": 169,
+  "feasible_trials": 167,
+  "best_trial": {
+    "number": 163,
+    "weighted_sum": 181.1220151783071,
+    "objectives": {
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+      "wfe_60_20": 13.198682506114679,
+      "mfg_90": 26.573840991950224,
+      "mass_kg": 96.75011491846891
+    },
+    "params": {
+      "lateral_inner_u": 0.32248417341983515,
+      "lateral_outer_u": 0.9038210727913156,
+      "lateral_middle_pivot": 21.25398896032501,
+      "lateral_inner_angle": 30.182447933329243,
+      "lateral_outer_angle": 15.08932828662093
+    },
+    "iter_folder": "C:\\Users\\antoi\\Atomizer\\studies\\M1_Mirror\\m1_mirror_cost_reduction_lateral\\2_iterations\\iter164"
+  },
+  "timestamp": "2026-01-14T17:59:38.649254"
+}
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/3_results/study.db
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/3_results/study.db
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/README.md
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/README.md
@@ -0,0 +1,136 @@
+# M1 Mirror Cost Reduction - Lateral Supports Optimization
+
+> See [../README.md](../README.md) for project overview and optical specifications.
+
+## Study Overview
+
+| Field | Value |
+|-------|-------|
+| **Study Name** | m1_mirror_cost_reduction_lateral |
+| **Algorithm** | CMA-ES |
+| **Status** | Ready to run |
+| **Created** | 2026-01-13 |
+| **Trials** | 100 planned |
+| **Focus** | Lateral support geometry only |
+
+## Purpose
+
+Optimize **lateral support parameters only** for the COST REDUCTION model using the new U-joint parameterization:
+- `lateral_inner_u` and `lateral_outer_u` replace the old `lateral_inner_pivot` and `lateral_outer_pivot`
+- All other parameters (whiffle, ribs, thickness) are **fixed at baseline values**
+- **Mass is NOT an objective** - only WFE and MFG are optimized
+
+## Design Variables (5)
+
+| Variable | Min | Max | Baseline | Units | Notes |
+|----------|-----|-----|----------|-------|-------|
+| `lateral_inner_u` | 0.2 | 0.95 | TBD | unitless | U-joint ratio for inner lateral (NEW) |
+| `lateral_outer_u` | 0.2 | 0.95 | TBD | unitless | U-joint ratio for outer lateral (NEW) |
+| `lateral_middle_pivot` | 15.0 | 27.0 | TBD | mm | Middle pivot position |
+| `lateral_inner_angle` | 25.0 | 35.0 | TBD | degrees | Inner lateral angle |
+| `lateral_outer_angle` | 8.0 | 17.0 | TBD | degrees | Outer lateral angle |
+
+**Note:** Baselines marked TBD will be updated after model introspection.
+
+## Objectives
+
+| Objective | Weight | Target | Description |
+|-----------|--------|--------|-------------|
+| `wfe_40_20` | 6.0 | 4.0 nm | Filtered RMS WFE at 40 deg relative to 20 deg |
+| `wfe_60_20` | 5.0 | 10.0 nm | Filtered RMS WFE at 60 deg relative to 20 deg |
+| `mfg_90` | 3.0 | 20.0 nm | Manufacturing deformation at 90 deg polishing |
+
+**Weighted Sum Formula:** `6*wfe_40_20 + 5*wfe_60_20 + 3*mfg_90`
+
+**Note:** Mass is **NOT** in the objective function. A hard constraint (mass <= 120 kg) is still enforced.
+
+## Fixed Parameters (Baked in Model)
+
+All non-lateral parameters are fixed at the model's current values. These are **not pushed** during optimization - the model already contains the correct values.
+
+## Why CMA-ES?
+
+| Factor | This Problem | Why CMA-ES |
+|--------|--------------|------------|
+| Dimensions | 5 variables | CMA-ES optimal for 5-50D |
+| Variable type | All continuous | CMA-ES designed for continuous |
+| Landscape | Smooth (physics-based) | CMA-ES exploits gradient structure |
+| Correlation | Lateral params likely correlated | CMA-ES learns correlations automatically |
+| Convergence | 100 trials budget | CMA-ES converges 2-3x faster than TPE |
+
+### CMA-ES Settings
+- `sigma0`: 0.3 (30% of range for initial exploration)
+- `restart_strategy`: IPOP (restarts with larger population if stuck)
+- `seed`: 42
+
+## Model Files
+
+Place the following files in `1_setup/model/`:
+
+| File | Purpose |
+|------|---------|
+| `ASSY_M1_assyfem1_sim1.sim` | Simulation file |
+| `*.fem` | FEM mesh files |
+| `*.prt` | Geometry parts |
+| `*_i.prt` | Idealized part (critical for mesh updates) |
+
+## Extraction Method
+
+- **Type:** ZernikeOPDExtractor with ANNULAR aperture
+- **Inner radius:** 135.75 mm (271.5 mm central hole excluded)
+- **Zernike modes:** 50
+- **Filter orders:** J1-J4 removed for WFE, J1-J3 for MFG
+- **Subcases:** 90 deg (1), 20 deg (2), 40 deg (3), 60 deg (4)
+- **Reference:** 20 deg (subcase 2)
+
+## Usage
+
+```bash
+# Single test trial
+python run_optimization.py --test
+
+# Full optimization (100 trials) - auto-launches dashboard
+python run_optimization.py --start
+
+# Custom trial count
+python run_optimization.py --start --trials 50
+
+# Resume interrupted run
+python run_optimization.py --start --resume
+
+# Without dashboard
+python run_optimization.py --start --no-dashboard
+```
+
+## Directory Structure
+
+```
+m1_mirror_cost_reduction_lateral/
+|-- 1_setup/
+|   |-- model/                    # NX model files (user to add)
+|   `-- optimization_config.json  # Study configuration
+|-- 2_iterations/                 # FEA iteration folders
+|-- 3_results/                    # Results database & summaries
+|   |-- study.db                  # Optuna SQLite database
+|   |-- optimization.log          # Run log
+|   `-- optimization_summary.json # Final results
+|-- run_optimization.py           # Main optimization script
+|-- README.md                     # This file
+`-- STUDY_REPORT.md               # Results template
+```
+
+## Setup Checklist
+
+- [ ] Copy model files to `1_setup/model/`
+- [ ] Run introspection to get baseline values
+- [ ] Update `optimization_config.json` with correct baselines
+- [ ] Run `--test` to verify setup
+- [ ] Run full optimization
+
+## Results
+
+*Study not yet run. Results will be updated after optimization completes.*
+
+## References
+
+- Sister study: [m1_mirror_flatback_lateral](../m1_mirror_flatback_lateral/) (same approach, flat back model)
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/STUDY_REPORT.md
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/STUDY_REPORT.md
@@ -0,0 +1,126 @@
+# Study Report: m1_mirror_cost_reduction_lateral
+
+## Executive Summary
+
+| Metric | Value |
+|--------|-------|
+| **Study Name** | m1_mirror_cost_reduction_lateral |
+| **Algorithm** | CMA-ES |
+| **Trials Completed** | _pending_ |
+| **Best Weighted Sum** | _pending_ |
+| **Constraint Satisfaction** | _pending_ |
+
+## Optimization Focus
+
+This study optimizes **lateral support parameters only** for the COST REDUCTION model using the new U-joint parameterization:
+- `lateral_inner_u`, `lateral_outer_u` (NEW - replace pivot params)
+- `lateral_middle_pivot`, `lateral_inner_angle`, `lateral_outer_angle`
+
+**Key Difference**: Mass is NOT an objective - only WFE and MFG are optimized.
+
+## Optimization Progress
+
+_To be filled after optimization run_
+
+### Convergence Plot
+
+_Insert convergence plot here_
+
+### Parameter Evolution
+
+_Insert parameter evolution plots here_
+
+## Best Designs Found
+
+### Top 5 Designs
+
+| Rank | Trial | WS | WFE 40/20 | WFE 60/20 | MFG 90 | Mass |
+|------|-------|-------|-----------|-----------|--------|------|
+| 1 | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ |
+| 2 | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ |
+| 3 | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ |
+| 4 | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ |
+| 5 | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ | _pending_ |
+
+### Best Design Parameters
+
+| Parameter | Baseline | Best | Change |
+|-----------|----------|------|--------|
+| lateral_inner_u | _TBD_ | _pending_ | _pending_ |
+| lateral_outer_u | _TBD_ | _pending_ | _pending_ |
+| lateral_middle_pivot | _TBD_ | _pending_ | _pending_ |
+| lateral_inner_angle | _TBD_ | _pending_ | _pending_ |
+| lateral_outer_angle | _TBD_ | _pending_ | _pending_ |
+
+## Parameter Sensitivity
+
+_To be filled after analysis_
+
+### Most Influential Parameters
+
+1. _pending_
+2. _pending_
+3. _pending_
+
+### Parameter Correlations
+
+_Insert correlation analysis_
+
+## Comparison to Baseline
+
+| Metric | Baseline | Best | Improvement |
+|--------|----------|------|-------------|
+| Weighted Sum | _pending_ | _pending_ | _pending_ |
+| WFE 40/20 | _pending_ | _pending_ | _pending_ |
+| WFE 60/20 | _pending_ | _pending_ | _pending_ |
+| MFG 90 | _pending_ | _pending_ | _pending_ |
+
+## Comparison to Flat Back Lateral Study
+
+| Metric | Flat Back | Cost Reduction | Difference |
+|--------|-----------|----------------|------------|
+| Best WS | _pending_ | _pending_ | _pending_ |
+| Best WFE 40/20 | _pending_ | _pending_ | _pending_ |
+| Best WFE 60/20 | _pending_ | _pending_ | _pending_ |
+
+## Key Learnings
+
+_To be filled after analysis_
+
+1. _pending_
+2. _pending_
+3. _pending_
+
+## Recommendations
+
+_To be filled after analysis_
+
+### For Next Study
+
+- [ ] _pending_
+
+### For Production
+
+- [ ] _pending_
+
+## Appendix
+
+### Run Configuration
+
+```json
+Algorithm: CMA-ES
+Trials: 100
+sigma0: 0.3
+restart_strategy: IPOP
+```
+
+### Files Generated
+
+- `3_results/study.db` - Optuna database
+- `3_results/optimization.log` - Run log
+- `3_results/optimization_summary.json` - Final results
+- `3_results/best_design_archive/` - Archived best designs
+
+---
+
+*Report generated: _pending_*
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/atomizer_spec.json
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/atomizer_spec.json
@@ -2,9 +2,9 @@
  "meta": {
    "version": "2.0",
    "created": "2026-01-17T15:35:12.024432Z",
-    "modified": "2026-01-20T19:01:36.016065Z",
+    "modified": "2026-01-20T20:05:28.197219Z",
    "created_by": "migration",
-    "modified_by": "canvas",
+    "modified_by": "test",
    "study_name": "m1_mirror_cost_reduction_lateral",
    "description": "Lateral support optimization with new U-joint expressions (lateral_inner_u, lateral_outer_u) for cost reduction model. Focus on WFE and MFG only - no mass objective.",
    "tags": [
@@ -151,6 +151,38 @@
        "x": 50,
        "y": 580
      }
+    },
+    {
+      "name": "variable_1768938898079",
+      "expression_name": "expr_1768938898079",
+      "type": "continuous",
+      "bounds": {
+        "min": 0,
+        "max": 1
+      },
+      "baseline": 0.5,
+      "enabled": true,
+      "canvas_position": {
+        "x": -185.06035488622524,
+        "y": 91.62521000204346
+      },
+      "id": "dv_008"
+    },
+    {
+      "name": "test_dv",
+      "expression_name": "test_expr",
+      "type": "continuous",
+      "bounds": {
+        "min": 0,
+        "max": 1
+      },
+      "baseline": 0.5,
+      "enabled": true,
+      "id": "dv_009",
+      "canvas_position": {
+        "x": 50,
+        "y": 680
+      }
    }
  ],
  "extractors": [
@@ -228,11 +260,11 @@
        "name": "extract_volume",
        "module": null,
        "signature": null,
-        "source_code": "def extract_volume(trial_dir, config, context):\n    \"\"\"\n    Extract volume from mass using material density.\n    Volume = Mass / Density\n    \n    For Zerodur glass-ceramic: density ~ 2530 kg/mÃ‚Â³\n    \"\"\"\n    import json\n    from pathlib import Path\n    \n    # Get mass from the mass extractor results\n    results_file = Path(trial_dir) / 'results.json'\n    if results_file.exists():\n        with open(results_file) as f:\n            results = json.load(f)\n        mass_kg = results.get('mass_kg', 0)\n    else:\n        # If no results yet, try to get from context\n        mass_kg = context.get('mass_kg', 0)\n    \n    density = config.get('density_kg_m3', 2530.0)  # Zerodur default\n    \n    # Volume in mÃ‚Â³\n    volume_m3 = mass_kg / density if density > 0 else 0\n    \n    # Also calculate in liters for convenience (1 mÃ‚Â³ = 1000 L)\n    volume_liters = volume_m3 * 1000000\n    \n    return {\n        'volume_m3': volume_m3,\n        'volume_liters': volume_liters\n    }\n"
+        "source_code": "\"\"\"Extract modal mass matrix from Nastran OP2 file\"\"\"\n\nfrom pyNastran.op2.op2 import OP2\nimport numpy as np\n\ndef extract(op2_path: str, fem_path: str, params: dict, subcase_id: int = 1) -> dict:\n    \"\"\"\n    Extract modal mass matrix from Nastran modal analysis results.\n    \n    The modal mass matrix (generalized mass) is typically diagonal for\n    mass-normalized modes, with each diagonal entry representing the\n    effective mass participation for that mode.\n    \n    Returns:\n        dict with keys:\n        - modal_mass_1, modal_mass_2, modal_mass_3: First three modal masses\n        - total_modal_mass: Sum of all modal masses\n        - modal_mass_matrix: Full diagonal modal mass array (as list)\n    \"\"\"\n    op2 = OP2()\n    op2.read_op2(op2_path)\n    \n    # Initialize outputs\n    modal_mass_1 = 0.0\n    modal_mass_2 = 0.0\n    modal_mass_3 = 0.0\n    total_modal_mass = 0.0\n    modal_mass_matrix = []\n    \n    try:\n        # Method 1: Check for modal participation factors / effective mass\n        # This is stored in op2.modal_contribution if available\n        if hasattr(op2, 'modal_contribution') and op2.modal_contribution:\n            mc = op2.modal_contribution\n            if subcase_id in mc:\n                modal_data = mc[subcase_id]\n                if hasattr(modal_data, 'effective_mass'):\n                    eff_mass = modal_data.effective_mass\n                    modal_mass_matrix = eff_mass.tolist() if hasattr(eff_mass, 'tolist') else list(eff_mass)\n        \n        # Method 2: Check eigenvalues for generalized mass\n        # pyNastran stores generalized mass in eigenvalues object\n        if not modal_mass_matrix and subcase_id in op2.eigenvalues:\n            eig = op2.eigenvalues[subcase_id]\n            \n            # Generalized mass is typically stored as 'generalized_mass' attribute\n            if hasattr(eig, 'generalized_mass'):\n                gen_mass = eig.generalized_mass\n                modal_mass_matrix = gen_mass.tolist() if hasattr(gen_mass, 'tolist') else list(gen_mass)\n            \n            # Alternative: mass-normalized modes have unit modal mass\n            # Check the mode_cycle attribute for mass normalization info\n            elif hasattr(eig, 'mass'):\n                mass = eig.mass\n                modal_mass_matrix = mass.tolist() if hasattr(mass, 'tolist') else list(mass)\n        \n        # Method 3: For mass-normalized eigenvectors, modal mass = 1.0\n        # Check if eigenvectors exist and compute modal mass from them\n        if not modal_mass_matrix and subcase_id in op2.eigenvectors:\n            eigvec = op2.eigenvectors[subcase_id]\n            n_modes = eigvec.data.shape[1] if len(eigvec.data.shape) > 1 else 1\n            # For mass-normalized modes, modal mass is unity\n            modal_mass_matrix = [1.0] * n_modes\n        \n        # Extract individual modal masses\n        if modal_mass_matrix:\n            if len(modal_mass_matrix) >= 1:\n                modal_mass_1 = float(modal_mass_matrix[0])\n            if len(modal_mass_matrix) >= 2:\n                modal_mass_2 = float(modal_mass_matrix[1])\n            if len(modal_mass_matrix) >= 3:\n                modal_mass_3 = float(modal_mass_matrix[2])\n            total_modal_mass = float(sum(modal_mass_matrix))\n    \n    except Exception as e:\n        # Log error but return zeros gracefully\n        print(f\"Warning: Could not extract modal mass matrix: {e}\")\n    \n    return {\n        'modal_mass_1': modal_mass_1,\n        'modal_mass_2': modal_mass_2,\n        'modal_mass_3': modal_mass_3,\n        'total_modal_mass': total_modal_mass,\n        'modal_mass_matrix': modal_mass_matrix,\n    }"
      }
    },
    {
-      "name": "a1768934465995fsfdadd",
+      "name": "extractor_1768938758443",
      "type": "custom_function",
      "builtin": false,
      "enabled": true,
@@ -247,31 +279,31 @@
        }
      ],
      "canvas_position": {
-        "x": 661.4703818070815,
-        "y": 655.713625352519
-      },
-      "id": "ext_004"
-    },
-    {
-      "name": "extractor_1768934622682",
-      "type": "custom_function",
-      "builtin": false,
-      "enabled": true,
-      "function": {
-        "name": "extract",
-        "source_code": "def extract(op2_path: str, config: dict = None) -> dict:\n    \"\"\"\n    Custom extractor function.\n    \n    Args:\n        op2_path: Path to the OP2 results file\n        config: Optional configuration dict\n    \n    Returns:\n        Dictionary with extracted values\n    \"\"\"\n    # TODO: Implement extraction logic\n    return {'value': 0.0}\n"
-      },
-      "outputs": [
-        {
-          "name": "value",
-          "metric": "custom"
-        }
-      ],
-      "canvas_position": {
-        "x": 588.8370255010856,
-        "y": 516.8654070156841
+        "x": 522.740988960073,
+        "y": 560.0208026883463
      },
      "id": "ext_005"
+    },
+    {
+      "name": "extractor_1768938897219",
+      "type": "custom_function",
+      "builtin": false,
+      "enabled": true,
+      "function": {
+        "name": "extract",
+        "source_code": "def extract(op2_path: str, config: dict = None) -> dict:\n    \"\"\"\n    Custom extractor function.\n    \n    Args:\n        op2_path: Path to the OP2 results file\n        config: Optional configuration dict\n    \n    Returns:\n        Dictionary with extracted values\n    \"\"\"\n    # TODO: Implement extraction logic\n    return {'value': 0.0}\n"
+      },
+      "outputs": [
+        {
+          "name": "value",
+          "metric": "custom"
+        }
+      ],
+      "canvas_position": {
+        "x": -197.5451097726711,
+        "y": 262.2501934501369
+      },
+      "id": "ext_004"
    }
  ],
  "objectives": [
@@ -418,10 +450,6 @@
        "source": "ext_001",
        "target": "obj_003"
      },
-      {
-        "source": "ext_002",
-        "target": "con_001"
-      },
      {
        "source": "obj_001",
        "target": "optimization"
@@ -437,6 +465,10 @@
      {
        "source": "con_001",
        "target": "optimization"
+      },
+      {
+        "source": "ext_002",
+        "target": "con_001"
      }
    ],
    "layout_version": "2.0"
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/introspect_model.py
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/introspect_model.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+"""Introspect M1_Blank.prt to get current expression values."""
+import sys
+import json
+sys.path.insert(0, 'c:/Users/antoi/Atomizer')
+
+from optimization_engine.extractors.introspect_part import introspect_part, get_expressions_dict
+
+MODEL_PATH = 'c:/Users/antoi/Atomizer/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model/M1_Blank.prt'
+OUTPUT_DIR = 'c:/Users/antoi/Atomizer/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model'
+
+# Expressions we care about
+LATERAL_VARS = [
+    'lateral_inner_u',
+    'lateral_outer_u',
+    'lateral_middle_pivot',
+    'lateral_inner_angle',
+    'lateral_outer_angle',
+    'lateral_closeness',
+]
+
+print("Running NX introspection on M1_Blank.prt (cost reduction model)...")
+result = introspect_part(MODEL_PATH, OUTPUT_DIR, verbose=True)
+
+if result.get('success'):
+    exprs = get_expressions_dict(result)
+
+    print()
+    print("=" * 60)
+    print("DESIGN VARIABLES (lateral) - current values in model:")
+    print("=" * 60)
+    for name in LATERAL_VARS:
+        if name in exprs:
+            print(f"  {name}: {exprs[name]}")
+        else:
+            print(f"  {name}: NOT FOUND")
+
+    # Save all expressions to JSON for easy reference
+    output_json = 'c:/Users/antoi/Atomizer/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/1_setup/model_expressions.json'
+    with open(output_json, 'w') as f:
+        json.dump(exprs, f, indent=2)
+    print(f"\nAll expressions saved to: {output_json}")
+else:
+    print(f"Introspection failed: {result.get('error', 'Unknown error')}")
--- a/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/run_optimization.py
+++ b/studies/M1_Mirror/m1_mirror_cost_reduction_lateral/run_optimization.py
@@ -0,0 +1,695 @@
+#!/usr/bin/env python3
+"""
+M1 Mirror Cost Reduction Lateral - Lateral Supports Optimization (CMA-ES)
+==========================================================================
+
+Lateral support optimization for COST REDUCTION model with new U-joint expressions:
+- lateral_inner_u (replaces lateral_inner_pivot)
+- lateral_outer_u (replaces lateral_outer_pivot)
+- lateral_middle_pivot (unchanged)
+- lateral_inner_angle (unchanged)
+- lateral_outer_angle (unchanged)
+
+Key Features:
+1. CMA-ES sampler - ideal for 5D continuous optimization
+2. ANNULAR APERTURE - excludes 271.5mm central hole from Zernike fitting
+3. Uses ZernikeOPDExtractor.extract_relative() with inner_radius=135.75mm
+4. Weighted sum: 6*wfe_40_20 + 5*wfe_60_20 + 3*mfg_90 (NO mass objective)
+5. Hard constraint: mass <= 120 kg (still enforced)
+
+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: 2026-01-13
+"""
+
+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 is at studies/M1_Mirror/study_name/)
+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:
+        # Launch dashboard in background (detached process)
+        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 CmaEsSampler
+
+# Atomizer imports
+from optimization_engine.nx.solver import NXSolver
+from optimization_engine.extractors import ZernikeOPDExtractor  # Supports annular apertures
+
+# ============================================================================
+# 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"]
+
+# Objective weights from config (NO MASS!)
+OBJ_WEIGHTS = {
+    'wfe_40_20': 6.0,
+    'wfe_60_20': 5.0,
+    'mfg_90': 3.0
+}
+
+# Hard constraint: blank_mass <= 120kg (still enforced even though not optimizing)
+MAX_BLANK_MASS_KG = 120.0
+CONSTRAINT_PENALTY = 1e6
+
+# ANNULAR APERTURE: 271.5mm central hole diameter -> 135.75mm radius
+INNER_RADIUS_MM = CONFIG.get('extraction_method', {}).get('inner_radius', 135.75)
+
+
+def compute_weighted_sum(objectives: Dict[str, float]) -> float:
+    """Compute weighted sum of objectives (NO MASS!)."""
+    return (OBJ_WEIGHTS['wfe_40_20'] * objectives.get('wfe_40_20', 1000.0) +
+            OBJ_WEIGHTS['wfe_60_20'] * objectives.get('wfe_60_20', 1000.0) +
+            OBJ_WEIGHTS['mfg_90'] * objectives.get('mfg_90', 1000.0))
+
+
+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 Annular Zernike Extraction
+# ============================================================================
+
+class FEARunner:
+    """Runs FEA simulations with annular aperture Zernike 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 to apply on every run
+        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', 'm1_mirror_flatback_lateral')
+
+        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 ZernikeOPDExtractor with annular aperture."""
+        if self.nx_solver is None:
+            self.setup()
+
+        logger.info(f"  [FEA {trial_num}] Running simulation...")
+
+        # Build expressions: start with fixed params, then add optimization params
+        expressions = {}
+
+        # Fixed parameters
+        for name, value in self.fixed_params.items():
+            expressions[name] = value
+
+        # Optimization variables (the ones we're actually varying)
+        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 ZernikeOPDExtractor with ANNULAR APERTURE
+            op2_path = Path(result['op2_file'])
+            objectives, lateral_diag = self._extract_objectives_annular(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}] 40-20: {objectives['wfe_40_20']:.2f} nm (Annular OPD)")
+            logger.info(f"  [FEA {trial_num}] 60-20: {objectives['wfe_60_20']:.2f} nm (Annular OPD)")
+            logger.info(f"  [FEA {trial_num}] Mfg:   {objectives['mfg_90']:.2f} nm (Annular OPD)")
+            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_ZernikeOPD_Annular',
+                'solve_time': solve_time,
+                'iter_folder': str(iter_folder),
+                'lateral_diagnostics': lateral_diag
+            }
+
+        except Exception as e:
+            logger.error(f"  [FEA {trial_num}] Error: {e}")
+            import traceback
+            traceback.print_exc()
+            return None
+
+    def _extract_objectives_annular(self, op2_path: Path, iter_folder: Path) -> tuple:
+        """
+        Extract objectives using ZernikeOPDExtractor with ANNULAR APERTURE.
+
+        The central hole (271.5mm diameter, inner_radius=135.75mm) is EXCLUDED
+        from Zernike fitting and RMS calculations.
+        """
+        try:
+            zernike_settings = self.config.get('zernike_settings', {})
+
+            # Create ZernikeOPDExtractor with ANNULAR APERTURE
+            extractor = ZernikeOPDExtractor(
+                op2_path,
+                figure_path=None,  # Uses BDF geometry
+                bdf_path=None,     # Auto-detected
+                displacement_unit=zernike_settings.get('displacement_unit', 'mm'),
+                n_modes=zernike_settings.get('n_modes', 50),
+                filter_orders=zernike_settings.get('filter_low_orders', 4),
+                inner_radius=INNER_RADIUS_MM  # ANNULAR APERTURE!
+            )
+
+            ref = zernike_settings.get('reference_subcase', '2')
+
+            # Extract RELATIVE metrics with annular masking
+            rel_40 = extractor.extract_relative("3", ref)  # 40 deg vs 20 deg
+            rel_60 = extractor.extract_relative("4", ref)  # 60 deg vs 20 deg
+            rel_90 = extractor.extract_relative("1", ref)  # 90 deg vs 20 deg (for MFG)
+
+            # Log annular info
+            if 'obscuration_ratio' in rel_40:
+                logger.info(f"  [Annular] Inner R={INNER_RADIUS_MM:.1f}mm, Obscuration={rel_40['obscuration_ratio']*100:.1f}%")
+                logger.info(f"  [Annular] Using {rel_40.get('n_annular_nodes', '?')} nodes (excl. central hole)")
+
+            # 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}")
+
+            # Also check _temp_part_properties.json
+            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
+
+            lateral_diag = {
+                'max_um': rel_40.get('max_lateral_displacement_um', 0),
+                'rms_um': rel_40.get('rms_lateral_displacement_um', 0),
+            }
+
+            objectives = {
+                'wfe_40_20': rel_40['relative_filtered_rms_nm'],
+                'wfe_60_20': rel_60['relative_filtered_rms_nm'],
+                'mfg_90': rel_90['relative_rms_filter_j1to3'],
+                'mass_kg': mass_kg
+            }
+
+            return objectives, lateral_diag
+
+        except Exception as e:
+            logger.error(f"Annular Zernike extraction failed: {e}")
+            import traceback
+            traceback.print_exc()
+            return None, {}
+
+
+# ============================================================================
+# CMA-ES Optimizer
+# ============================================================================
+
+class CMAESOptimizer:
+    """CMA-ES optimizer for lateral support parameters."""
+
+    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 (only enabled variables)
+        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)
+        }
+
+        # CMA-ES settings
+        opt_settings = config.get('optimization', {})
+        self.sigma0 = opt_settings.get('sigma0', 0.3)
+        self.seed = opt_settings.get('seed', 42)
+
+        # Study
+        self.study_name = config.get('study_name', 'm1_mirror_flatback_lateral')
+        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'):
+                    try:
+                        num = int(d.name.replace('iter', ''))
+                        self.fea_count = max(self.fea_count, num)
+                    except ValueError:
+                        pass
+        logger.info(f"Existing FEA iterations: {self.fea_count}")
+
+    def create_study(self) -> optuna.Study:
+        """Create or load Optuna study with CMA-ES sampler."""
+        # Get baseline values for x0 (starting point)
+        x0 = {}
+        for name, bounds in self.design_vars.items():
+            x0[name] = bounds['baseline']
+
+        sampler = CmaEsSampler(
+            x0=x0,
+            sigma0=self.sigma0,
+            seed=self.seed,
+            restart_strategy='ipop'
+        )
+
+        storage = f"sqlite:///{self.db_path}"
+
+        if self.resume:
+            try:
+                study = optuna.load_study(
+                    study_name=self.study_name,
+                    storage=storage,
+                    sampler=sampler
+                )
+                logger.info(f"Resumed study with {len(study.trials)} existing trials")
+
+                # Find current best
+                completed = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]
+                feasible = [t for t in completed if t.user_attrs.get('is_feasible', False)]
+                if feasible:
+                    best = min(feasible, key=lambda t: t.value if t.value else float('inf'))
+                    if best.value:
+                        self.best_weighted_sum = best.value
+                        logger.info(f"Current best (feasible): {self.best_weighted_sum:.2f}")
+
+                return study
+            except KeyError:
+                logger.info("No existing study found, creating new one")
+
+        study = optuna.create_study(
+            study_name=self.study_name,
+            storage=storage,
+            sampler=sampler,
+            direction="minimize",
+            load_if_exists=True
+        )
+
+        # Enqueue baseline as first trial
+        if len(study.trials) == 0:
+            logger.info("Enqueueing baseline as trial 0...")
+            study.enqueue_trial(x0)
+
+        return study
+
+    def objective(self, trial: optuna.Trial) -> float:
+        """CMA-ES objective function."""
+        # Sample parameters
+        params = {}
+        for name, bounds in self.design_vars.items():
+            params[name] = trial.suggest_float(name, bounds['min'], bounds['max'])
+
+        # Increment FEA counter
+        self.fea_count += 1
+        iter_num = self.fea_count
+
+        logger.info(f"Trial {trial.number} -> iter{iter_num}")
+        for name, value in params.items():
+            logger.info(f"  {name} = {value:.3f}")
+
+        # Run FEA
+        result = self.fea_runner.run_fea(params, iter_num)
+
+        if result is None:
+            trial.set_user_attr('source', 'FEA_FAILED')
+            trial.set_user_attr('iter_num', iter_num)
+            trial.set_user_attr('is_feasible', False)
+            return 1e6
+
+        # Store metadata
+        trial.set_user_attr('source', 'FEA_ZernikeOPD_Annular')
+        trial.set_user_attr('iter_num', iter_num)
+        trial.set_user_attr('iter_folder', result['iter_folder'])
+        trial.set_user_attr('wfe_40_20', result['objectives']['wfe_40_20'])
+        trial.set_user_attr('wfe_60_20', result['objectives']['wfe_60_20'])
+        trial.set_user_attr('mfg_90', result['objectives']['mfg_90'])
+        trial.set_user_attr('mass_kg', result['objectives']['mass_kg'])
+        trial.set_user_attr('solve_time', result['solve_time'])
+        trial.set_user_attr('is_feasible', result['is_feasible'])
+
+        weighted_sum = result['weighted_sum']
+
+        # Check if new best
+        if result['is_feasible'] and weighted_sum < self.best_weighted_sum:
+            logger.info(f"  NEW BEST! {weighted_sum:.2f} (was {self.best_weighted_sum:.2f})")
+            self.best_weighted_sum = weighted_sum
+            self.best_trial_info = {
+                'trial_number': trial.number,
+                'iter_num': iter_num,
+                'iter_folder': result['iter_folder'],
+                'weighted_sum': weighted_sum,
+                'objectives': result['objectives'],
+                'params': params
+            }
+            self._archive_best_design()
+
+        return weighted_sum
+
+    def _archive_best_design(self):
+        """Archive current best design."""
+        if self.best_trial_info is None:
+            return
+
+        try:
+            archive_dir = RESULTS_DIR / "best_design_archive"
+            archive_dir.mkdir(exist_ok=True)
+
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            dest_dir = archive_dir / timestamp
+
+            src_dir = Path(self.best_trial_info['iter_folder'])
+            if src_dir.exists():
+                shutil.copytree(src_dir, dest_dir)
+
+                info = {
+                    'study_name': self.study_name,
+                    'trial_number': self.best_trial_info['trial_number'],
+                    'iteration_folder': f"iter{self.best_trial_info['iter_num']}",
+                    'weighted_sum': self.best_trial_info['weighted_sum'],
+                    'objectives': self.best_trial_info['objectives'],
+                    'params': self.best_trial_info['params'],
+                    'extraction_method': 'ZernikeOPD_Annular (271.5mm central hole excluded)',
+                    'inner_radius_mm': INNER_RADIUS_MM,
+                    'archived_at': datetime.now().isoformat()
+                }
+                with open(dest_dir / '_archive_info.json', 'w') as f:
+                    json.dump(info, f, indent=2)
+
+                logger.info(f"  Archived to: {dest_dir.name}")
+
+        except Exception as e:
+            logger.warning(f"Could not archive best design: {e}")
+
+    def run(self, n_trials: int):
+        """Run CMA-ES optimization."""
+        study = self.create_study()
+
+        logger.info("=" * 70)
+        logger.info("M1 MIRROR FLAT BACK - LATERAL SUPPORTS OPTIMIZATION (CMA-ES)")
+        logger.info("=" * 70)
+        logger.info("*** ANNULAR APERTURE: 271.5mm central hole EXCLUDED ***")
+        logger.info(f"*** Inner radius: {INNER_RADIUS_MM} mm ***")
+        logger.info(f"Study: {self.study_name}")
+        logger.info("*** OBJECTIVES: WFE only (mass NOT in objective) ***")
+        logger.info(f"Total trials in DB: {len(study.trials)}")
+        logger.info(f"New FEA trials to run: {n_trials}")
+        logger.info(f"Active Design Variables: {len(self.design_vars)}")
+        for name, bounds in self.design_vars.items():
+            baseline = bounds.get('baseline', 'N/A')
+            logger.info(f"  - {name}: [{bounds['min']}, {bounds['max']}] (baseline: {baseline})")
+        logger.info(f"CONSTRAINT: blank_mass <= {MAX_BLANK_MASS_KG} kg")
+        logger.info(f"CMA-ES sigma0: {self.sigma0}")
+        logger.info("=" * 70)
+
+        try:
+            study.optimize(
+                self.objective,
+                n_trials=n_trials,
+                show_progress_bar=True,
+                gc_after_trial=True
+            )
+        except KeyboardInterrupt:
+            logger.info("Optimization interrupted by user")
+
+        self._report_results(study)
+        return study
+
+    def _report_results(self, study: optuna.Study):
+        """Report optimization results."""
+        logger.info("\n" + "=" * 70)
+        logger.info("OPTIMIZATION RESULTS (Annular Aperture)")
+        logger.info("=" * 70)
+
+        completed = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE and t.value < 1e5]
+        feasible = [t for t in completed if t.user_attrs.get('is_feasible', False)]
+
+        logger.info(f"\nTotal completed: {len(completed)}")
+        logger.info(f"Feasible (mass <= {MAX_BLANK_MASS_KG}kg): {len(feasible)}")
+
+        if not feasible:
+            logger.warning("No feasible trials found!")
+            return
+
+        sorted_trials = sorted(feasible, key=lambda t: t.value)
+
+        print(f"\n{'Trial':>6} | {'WS':>10} | {'40vs20':>10} | {'60vs20':>10} | {'MFG':>10} | {'Mass':>10} | Iter")
+        print("-" * 85)
+
+        for t in sorted_trials[:15]:
+            obj_40 = t.user_attrs.get('wfe_40_20', 0)
+            obj_60 = t.user_attrs.get('wfe_60_20', 0)
+            obj_mfg = t.user_attrs.get('mfg_90', 0)
+            obj_mass = t.user_attrs.get('mass_kg', 0)
+            iter_num = t.user_attrs.get('iter_num', '?')
+            print(f"{t.number:>6} | {t.value:>10.2f} | {obj_40:>10.2f} | {obj_60:>10.2f} | {obj_mfg:>10.2f} | {obj_mass:>10.3f} | iter{iter_num}")
+
+        best = sorted_trials[0]
+        logger.info(f"\nBEST FEASIBLE TRIAL: #{best.number}")
+        logger.info(f"  Weighted Sum: {best.value:.2f}")
+        logger.info(f"  40-20: {best.user_attrs.get('wfe_40_20', 0):.2f} nm")
+        logger.info(f"  60-20: {best.user_attrs.get('wfe_60_20', 0):.2f} nm")
+        logger.info(f"  MFG:   {best.user_attrs.get('mfg_90', 0):.2f} nm")
+        logger.info(f"  Mass:  {best.user_attrs.get('mass_kg', 0):.3f} kg")
+        logger.info(f"\n  Best Lateral Parameters:")
+        for k, v in best.params.items():
+            logger.info(f"    {k}: {v:.3f}")
+
+        # Save summary
+        results_summary = {
+            'study_name': self.study_name,
+            'algorithm': 'CMA-ES',
+            'extraction_method': 'ZernikeOPD_Annular',
+            'inner_radius_mm': INNER_RADIUS_MM,
+            'objectives_note': 'Mass NOT in objective - WFE only',
+            'total_trials': len(study.trials),
+            'feasible_trials': len(feasible),
+            'best_trial': {
+                'number': best.number,
+                'weighted_sum': best.value,
+                'objectives': {
+                    'wfe_40_20': best.user_attrs.get('wfe_40_20'),
+                    'wfe_60_20': best.user_attrs.get('wfe_60_20'),
+                    'mfg_90': best.user_attrs.get('mfg_90'),
+                    'mass_kg': best.user_attrs.get('mass_kg')
+                },
+                'params': dict(best.params),
+                'iter_folder': best.user_attrs.get('iter_folder')
+            },
+            'timestamp': datetime.now().isoformat()
+        }
+
+        with open(RESULTS_DIR / 'optimization_summary.json', 'w') as f:
+            json.dump(results_summary, f, indent=2)
+
+        logger.info(f"\nResults saved to: {RESULTS_DIR / 'optimization_summary.json'}")
+
+
+# ============================================================================
+# Main
+# ============================================================================
+
+def main():
+    parser = argparse.ArgumentParser(description="M1 Mirror Cost Reduction Lateral - Lateral Supports Optimization")
+    parser.add_argument("--start", action="store_true", help="Start optimization")
+    parser.add_argument("--trials", type=int, default=100, help="Number of FEA trials")
+    parser.add_argument("--resume", action="store_true", help="Resume interrupted run")
+    parser.add_argument("--test", action="store_true", help="Run single test trial")
+    parser.add_argument("--no-dashboard", action="store_true", help="Don't auto-launch dashboard")
+
+    args = parser.parse_args()
+
+    if not args.start and not args.test:
+        parser.print_help()
+        print("\nUse --start to begin optimization or --test for single trial")
+        print("\n*** Optimizing LATERAL SUPPORT parameters only ***")
+        print("*** Design Variables: lateral_inner_u, lateral_outer_u, lateral_middle_pivot, ***")
+        print("***                   lateral_inner_angle, lateral_outer_angle ***")
+        print("*** Objectives: WFE only (mass NOT included) ***")
+        print("*** Using ANNULAR APERTURE - central hole excluded from Zernike fitting ***")
+        return
+
+    if not CONFIG_PATH.exists():
+        print(f"Error: Config not found at {CONFIG_PATH}")
+        sys.exit(1)
+
+    # Auto-launch dashboard (unless disabled)
+    if not args.no_dashboard:
+        launch_dashboard()
+        time.sleep(2)  # Give dashboard time to start
+
+    with open(CONFIG_PATH) as f:
+        config = json.load(f)
+
+    optimizer = CMAESOptimizer(config, resume=args.resume)
+
+    n_trials = 1 if args.test else args.trials
+    optimizer.run(n_trials=n_trials)
+
+
+if __name__ == "__main__":
+    main()
--- a/studies/M1_Mirror/m1_mirror_flatback_lateral/3_results/optimization_summary.json
+++ b/studies/M1_Mirror/m1_mirror_flatback_lateral/3_results/optimization_summary.json
@@ -4,25 +4,25 @@
  "extraction_method": "ZernikeOPD_Annular",
  "inner_radius_mm": 135.75,
  "objectives_note": "Mass NOT in objective - WFE only",
-  "total_trials": 1,
-  "feasible_trials": 1,
+  "total_trials": 101,
+  "feasible_trials": 100,
  "best_trial": {
-    "number": 0,
-    "weighted_sum": 341.40717511411987,
+    "number": 76,
+    "weighted_sum": 220.12317796085603,
    "objectives": {
-      "wfe_40_20": 9.738648075724171,
-      "wfe_60_20": 24.138392317227122,
-      "mfg_90": 54.09444169121308,
-      "mass_kg": 102.89579477048632
+      "wfe_40_20": 7.033921022459454,
+      "wfe_60_20": 16.109562572565014,
+      "mfg_90": 32.457279654424745,
+      "mass_kg": 102.89579477048622
    },
    "params": {
-      "lateral_inner_u": 0.4,
-      "lateral_outer_u": 0.4,
-      "lateral_middle_pivot": 22.42,
-      "lateral_inner_angle": 31.96,
-      "lateral_outer_angle": 9.08
+      "lateral_inner_u": 0.40304412850085514,
+      "lateral_outer_u": 0.9043062289622721,
+      "lateral_middle_pivot": 25.869245488671304,
+      "lateral_inner_angle": 32.008659765295675,
+      "lateral_outer_angle": 13.952742709877848
    },
-    "iter_folder": "C:\\Users\\antoi\\Atomizer\\studies\\M1_Mirror\\m1_mirror_flatback_lateral\\2_iterations\\iter1"
+    "iter_folder": "C:\\Users\\antoi\\Atomizer\\studies\\M1_Mirror\\m1_mirror_flatback_lateral\\2_iterations\\iter77"
  },
-  "timestamp": "2026-01-13T11:01:22.360549"
+  "timestamp": "2026-01-13T18:41:14.992549"
 }
--- a/studies/M1_Mirror/m1_mirror_flatback_lateral/3_results/study.db
+++ b/studies/M1_Mirror/m1_mirror_flatback_lateral/3_results/study.db
--- a/studies/M1_Mirror/m1_mirror_flatback_lateral/atomizer_spec.json
+++ b/studies/M1_Mirror/m1_mirror_flatback_lateral/atomizer_spec.json
@@ -2,9 +2,9 @@
  "meta": {
    "version": "2.0",
    "created": "2026-01-17T15:35:12.034330Z",
-    "modified": "2026-01-17T15:35:12.034330Z",
+    "modified": "2026-01-20T18:24:29.805432Z",
    "created_by": "migration",
-    "modified_by": "migration",
+    "modified_by": "canvas",
    "study_name": "m1_mirror_flatback_lateral",
    "description": "Lateral support optimization with new U-joint expressions (lateral_inner_u, lateral_outer_u). Focus on WFE and MFG only - no mass objective.",
    "tags": [
@@ -104,10 +104,10 @@
      "expression_name": "lateral_outer_angle",
      "type": "continuous",
      "bounds": {
-        "min": 8.0,
-        "max": 17.0
+        "min": 8,
+        "max": 17
      },
-      "baseline": 9.08,
+      "baseline": 10,
      "units": "degrees",
      "enabled": true,
      "description": "Outer lateral support angle",