docs: Major documentation overhaul - restructure folders, update tagline, add Getting Started guide

- Restructure docs/ folder (remove numeric prefixes):
  - 04_USER_GUIDES -> guides/
  - 05_API_REFERENCE -> api/
  - 06_PHYSICS -> physics/
  - 07_DEVELOPMENT -> development/
  - 08_ARCHIVE -> archive/
  - 09_DIAGRAMS -> diagrams/

- Replace tagline 'Talk, don't click' with 'LLM-driven optimization framework' in 9 files

- Create comprehensive docs/GETTING_STARTED.md:
  - Prerequisites and quick setup
  - Project structure overview
  - First study tutorial (Claude or manual)
  - Dashboard usage guide
  - Neural acceleration introduction

- Rewrite docs/00_INDEX.md with correct paths and modern structure

- Archive obsolete files:
  - 01_PROTOCOLS.md -> archive/historical/01_PROTOCOLS_legacy.md
  - 03_GETTING_STARTED.md -> archive/historical/
  - ATOMIZER_PODCAST_BRIEFING.md -> archive/marketing/

- Update timestamps to 2026-01-20 across all key files

- Update .gitignore to exclude docs/generated/

- Version bump: ATOMIZER_CONTEXT v1.8 -> v2.0
This commit is contained in:
2026-01-20 10:03:45 -05:00
parent 37f73cc2be
commit ea437d360e
103 changed files with 8980 additions and 327 deletions

View File

@@ -0,0 +1,635 @@
# Atomizer Dashboard Improvement Plan
## Executive Summary
This document outlines a comprehensive plan to enhance the Atomizer dashboard into a self-contained, professional optimization platform with integrated AI assistance through Claude Code.
---
## Current State
### Existing Pages
- **Home** (`/`): Study selection with README preview
- **Dashboard** (`/dashboard`): Real-time monitoring, charts, control panel
- **Results** (`/results`): AI-generated report viewer
### Existing Features
- Study selection with persistence
- README display on study hover
- Convergence plot (Plotly)
- Pareto plot for multi-objective
- Parallel coordinates
- Parameter importance chart
- Console output viewer
- Control panel (start/stop/validate)
- Optuna dashboard launch
- AI report generation
---
## Proposed Improvements
### Phase 1: Core UX Enhancements
#### 1.1 Unified Navigation & Branding
- **Logo & Brand Identity**: Professional Atomizer logo in sidebar
- **Breadcrumb Navigation**: Show current path (e.g., `Atomizer > m1_mirror > Dashboard`)
- **Quick Study Switcher**: Dropdown in header to switch studies without returning to Home
- **Keyboard Shortcuts**: `Ctrl+K` for command palette, `Ctrl+1/2/3` for page navigation
#### 1.2 Study Overview Card (Home Page Enhancement)
When a study is selected, show a summary card with:
- Trial progress ring/chart
- Best objective value with trend indicator
- Last activity timestamp
- Quick action buttons (Start, Validate, Open)
- Thumbnail preview of convergence
#### 1.3 Real-Time Status Indicators
- **Global Status Bar**: Shows running processes, current trial, ETA
- **Live Toast Notifications**: Trial completed, error occurred, validation done
- **Sound Notifications** (optional): Audio cue on trial completion
#### 1.4 Dark/Light Theme Toggle
- Persist theme preference in localStorage
- System theme detection
---
### Phase 2: Advanced Visualization
#### 2.1 Interactive Trial Table
- Sortable/filterable data grid with all trial data
- Column visibility toggles
- Export to CSV/Excel
- Click row to highlight in plots
- Filter by FEA vs Neural trials
#### 2.2 Enhanced Charts
- **Zoomable Convergence**: Brushing to select time ranges
- **3D Parameter Space**: Three.js visualization of design space
- **Heatmap**: Parameter correlation matrix
- **Animation**: Play through optimization history
#### 2.3 Comparison Mode
- Side-by-side comparison of 2-3 trials
- Diff view for parameter values
- Overlay plots
#### 2.4 Design Space Explorer
- Interactive sliders for design variables
- Predict objective using neural surrogate
- "What-if" analysis without running FEA
---
### Phase 3: Claude Code Integration (AI Chat)
#### 3.1 Architecture Overview
```
┌─────────────────────────────────────────────────────────────┐
│ Atomizer Dashboard │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────┐ ┌──────────────────────────┐ │
│ │ │ │ │ │
│ │ Main Dashboard │ │ Claude Code Panel │ │
│ │ (Charts, Controls) │ │ (Chat Interface) │ │
│ │ │ │ │ │
│ │ │ │ ┌────────────────────┐ │ │
│ │ │ │ │ Conversation │ │ │
│ │ │ │ │ History │ │ │
│ │ │ │ │ │ │ │
│ │ │ │ └────────────────────┘ │ │
│ │ │ │ │ │
│ │ │ │ ┌────────────────────┐ │ │
│ │ │ │ │ Input Box │ │ │
│ │ │ │ └────────────────────┘ │ │
│ │ │ │ │ │
│ └─────────────────────────┘ └──────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
┌─────────────────┐
│ Backend API │
│ /api/claude │
└────────┬────────┘
┌─────────────────┐
│ Claude Agent │
│ SDK Backend │
│ (Python) │
└────────┬────────┘
┌────────┴────────┐
│ │
┌────▼────┐ ┌─────▼─────┐
│ Atomizer│ │ Anthropic │
│ Tools │ │ Claude API│
└─────────┘ └───────────┘
```
#### 3.2 Backend Implementation
**New API Endpoints:**
```python
# atomizer-dashboard/backend/api/routes/claude.py
@router.post("/chat")
async def chat_with_claude(request: ChatRequest):
"""
Send a message to Claude with study context
Request:
- message: User's message
- study_id: Current study context
- conversation_id: For multi-turn conversations
Returns:
- response: Claude's response
- actions: Any tool calls made (file edits, commands)
"""
@router.websocket("/chat/stream")
async def chat_stream(websocket: WebSocket):
"""
WebSocket for streaming Claude responses
Real-time token streaming for better UX
"""
@router.get("/conversations")
async def list_conversations():
"""Get conversation history for current study"""
@router.delete("/conversations/{conversation_id}")
async def delete_conversation(conversation_id: str):
"""Delete a conversation"""
```
**Claude Agent SDK Integration:**
```python
# atomizer-dashboard/backend/services/claude_agent.py
from anthropic import Anthropic
import json
class AtomizerClaudeAgent:
def __init__(self, study_id: str = None):
self.client = Anthropic()
self.study_id = study_id
self.tools = self._load_atomizer_tools()
self.system_prompt = self._build_system_prompt()
def _build_system_prompt(self) -> str:
"""Build context-aware system prompt"""
prompt = """You are Claude Code embedded in the Atomizer optimization dashboard.
You have access to the current optimization study and can help users:
1. Analyze optimization results
2. Modify study configurations
3. Create new studies
4. Explain FEA/Zernike concepts
5. Suggest design improvements
Current Study Context:
{study_context}
Available Tools:
- read_study_config: Read optimization configuration
- modify_config: Update design variables, objectives
- query_trials: Get trial data from database
- create_study: Create new optimization study
- run_analysis: Perform custom analysis
- edit_file: Modify study files
"""
if self.study_id:
prompt = prompt.format(study_context=self._get_study_context())
else:
prompt = prompt.format(study_context="No study selected")
return prompt
def _load_atomizer_tools(self) -> list:
"""Define Atomizer-specific tools for Claude"""
return [
{
"name": "read_study_config",
"description": "Read the optimization configuration for the current study",
"input_schema": {
"type": "object",
"properties": {},
"required": []
}
},
{
"name": "query_trials",
"description": "Query trial data from the Optuna database",
"input_schema": {
"type": "object",
"properties": {
"filter": {
"type": "string",
"description": "SQL-like filter (e.g., 'state=COMPLETE')"
},
"limit": {
"type": "integer",
"description": "Max results to return"
}
}
}
},
{
"name": "modify_config",
"description": "Modify the optimization configuration",
"input_schema": {
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "JSON path to modify (e.g., 'design_variables[0].max')"
},
"value": {
"type": "any",
"description": "New value to set"
}
},
"required": ["path", "value"]
}
},
{
"name": "create_study",
"description": "Create a new optimization study",
"input_schema": {
"type": "object",
"properties": {
"name": {"type": "string"},
"description": {"type": "string"},
"model_path": {"type": "string"},
"design_variables": {"type": "array"},
"objectives": {"type": "array"}
},
"required": ["name"]
}
}
]
async def chat(self, message: str, conversation_history: list = None) -> dict:
"""Process a chat message with tool use"""
messages = conversation_history or []
messages.append({"role": "user", "content": message})
response = await self.client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=4096,
system=self.system_prompt,
tools=self.tools,
messages=messages
)
# Handle tool calls
if response.stop_reason == "tool_use":
tool_results = await self._execute_tools(response.content)
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": tool_results})
return await self.chat("", messages) # Continue conversation
return {
"response": response.content[0].text,
"conversation": messages + [{"role": "assistant", "content": response.content}]
}
```
#### 3.3 Frontend Implementation
**Chat Panel Component:**
```tsx
// atomizer-dashboard/frontend/src/components/ClaudeChat.tsx
import React, { useState, useRef, useEffect } from 'react';
import { Send, Bot, User, Sparkles, Loader2 } from 'lucide-react';
import ReactMarkdown from 'react-markdown';
import { useStudy } from '../context/StudyContext';
interface Message {
role: 'user' | 'assistant';
content: string;
timestamp: Date;
toolCalls?: any[];
}
export const ClaudeChat: React.FC = () => {
const { selectedStudy } = useStudy();
const [messages, setMessages] = useState<Message[]>([]);
const [input, setInput] = useState('');
const [isLoading, setIsLoading] = useState(false);
const messagesEndRef = useRef<HTMLDivElement>(null);
const sendMessage = async () => {
if (!input.trim() || isLoading) return;
const userMessage: Message = {
role: 'user',
content: input,
timestamp: new Date()
};
setMessages(prev => [...prev, userMessage]);
setInput('');
setIsLoading(true);
try {
const response = await fetch('/api/claude/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
message: input,
study_id: selectedStudy?.id,
conversation_history: messages
})
});
const data = await response.json();
setMessages(prev => [...prev, {
role: 'assistant',
content: data.response,
timestamp: new Date(),
toolCalls: data.tool_calls
}]);
} catch (error) {
// Handle error
} finally {
setIsLoading(false);
}
};
// Suggested prompts for new conversations
const suggestions = [
"Analyze my optimization results",
"What parameters have the most impact?",
"Create a new study for my bracket",
"Explain the Zernike coefficients"
];
return (
<div className="flex flex-col h-full bg-dark-800 rounded-xl border border-dark-600">
{/* Header */}
<div className="px-4 py-3 border-b border-dark-600 flex items-center gap-2">
<Bot className="w-5 h-5 text-primary-400" />
<span className="font-medium text-white">Claude Code</span>
{selectedStudy && (
<span className="text-xs bg-dark-700 px-2 py-0.5 rounded text-dark-300">
{selectedStudy.id}
</span>
)}
</div>
{/* Messages */}
<div className="flex-1 overflow-y-auto p-4 space-y-4">
{messages.length === 0 ? (
<div className="text-center py-8">
<Sparkles className="w-12 h-12 mx-auto mb-4 text-primary-400 opacity-50" />
<p className="text-dark-300 mb-4">Ask me anything about your optimization</p>
<div className="flex flex-wrap gap-2 justify-center">
{suggestions.map((s, i) => (
<button
key={i}
onClick={() => setInput(s)}
className="px-3 py-1.5 bg-dark-700 hover:bg-dark-600 rounded-lg
text-sm text-dark-300 hover:text-white transition-colors"
>
{s}
</button>
))}
</div>
</div>
) : (
messages.map((msg, i) => (
<div key={i} className={`flex gap-3 ${msg.role === 'user' ? 'justify-end' : ''}`}>
{msg.role === 'assistant' && (
<div className="w-8 h-8 rounded-lg bg-primary-600 flex items-center justify-center flex-shrink-0">
<Bot className="w-4 h-4 text-white" />
</div>
)}
<div className={`max-w-[80%] rounded-lg p-3 ${
msg.role === 'user'
? 'bg-primary-600 text-white'
: 'bg-dark-700 text-dark-200'
}`}>
<ReactMarkdown className="prose prose-sm prose-invert">
{msg.content}
</ReactMarkdown>
</div>
{msg.role === 'user' && (
<div className="w-8 h-8 rounded-lg bg-dark-600 flex items-center justify-center flex-shrink-0">
<User className="w-4 h-4 text-dark-300" />
</div>
)}
</div>
))
)}
{isLoading && (
<div className="flex gap-3">
<div className="w-8 h-8 rounded-lg bg-primary-600 flex items-center justify-center">
<Loader2 className="w-4 h-4 text-white animate-spin" />
</div>
<div className="bg-dark-700 rounded-lg p-3 text-dark-400">
Thinking...
</div>
</div>
)}
<div ref={messagesEndRef} />
</div>
{/* Input */}
<div className="p-4 border-t border-dark-600">
<div className="flex gap-2">
<input
type="text"
value={input}
onChange={(e) => setInput(e.target.value)}
onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
placeholder="Ask about your optimization..."
className="flex-1 px-4 py-2 bg-dark-700 border border-dark-600 rounded-lg
text-white placeholder-dark-400 focus:outline-none focus:border-primary-500"
/>
<button
onClick={sendMessage}
disabled={!input.trim() || isLoading}
className="px-4 py-2 bg-primary-600 hover:bg-primary-500 disabled:opacity-50
text-white rounded-lg transition-colors"
>
<Send className="w-4 h-4" />
</button>
</div>
</div>
</div>
);
};
```
#### 3.4 Claude Code Capabilities
When integrated, Claude Code will be able to:
| Capability | Description | Example Command |
|------------|-------------|-----------------|
| **Analyze Results** | Interpret optimization progress | "Why is my convergence plateauing?" |
| **Explain Physics** | Describe FEA/Zernike concepts | "Explain astigmatism in my mirror" |
| **Modify Config** | Update design variables | "Increase the max bounds for whiffle_min to 60" |
| **Create Studies** | Generate new study from description | "Create a study for my new bracket" |
| **Query Data** | SQL-like data exploration | "Show me the top 5 trials by stress" |
| **Generate Code** | Write custom analysis scripts | "Write a Python script to compare trials" |
| **Debug Issues** | Diagnose optimization problems | "Why did trial 42 fail?" |
---
### Phase 4: Study Creation Wizard
#### 4.1 Guided Study Setup
Multi-step wizard for creating new studies:
1. **Model Selection**
- Browse NX model files
- Auto-detect expressions
- Preview 3D geometry (if possible)
2. **Design Variables**
- Interactive table to set bounds
- Baseline detection from model
- Sensitivity hints from similar studies
3. **Objectives**
- Template selection (stress, displacement, frequency, Zernike)
- Direction (minimize/maximize)
- Target values and weights
4. **Constraints**
- Add geometric/physical constraints
- Feasibility preview
5. **Algorithm Settings**
- Protocol selection (10/11/12)
- Sampler configuration
- Neural surrogate options
6. **Review & Create**
- Summary of all settings
- Validation checks
- One-click creation
---
### Phase 5: Self-Contained Packaging
#### 5.1 Electron Desktop App
Package the dashboard as a standalone desktop application:
```
Atomizer.exe
├── Frontend (React bundled)
├── Backend (Python bundled with PyInstaller)
├── NX Integration (optional)
└── Claude API (requires key)
```
Benefits:
- No Node.js/Python installation needed
- Single installer for users
- Offline capability (except AI features)
- Native file dialogs
- System tray integration
#### 5.2 Docker Deployment
```yaml
# docker-compose.yml
version: '3.8'
services:
frontend:
build: ./atomizer-dashboard/frontend
ports:
- "3000:3000"
backend:
build: ./atomizer-dashboard/backend
ports:
- "8000:8000"
volumes:
- ./studies:/app/studies
environment:
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
```
---
## Implementation Priority
| Phase | Feature | Effort | Impact | Priority |
|-------|---------|--------|--------|----------|
| 1.1 | Unified Navigation | Medium | High | P1 |
| 1.2 | Study Overview Card | Low | High | P1 |
| 1.3 | Real-Time Status | Medium | High | P1 |
| 2.1 | Interactive Trial Table | Medium | High | P1 |
| 3.1 | Claude Chat Backend | High | Critical | P1 |
| 3.3 | Claude Chat Frontend | Medium | Critical | P1 |
| 2.2 | Enhanced Charts | Medium | Medium | P2 |
| 2.4 | Design Space Explorer | High | High | P2 |
| 4.1 | Study Creation Wizard | High | High | P2 |
| 5.1 | Electron Packaging | High | Medium | P3 |
---
## Technical Requirements
### Dependencies to Add
**Backend:**
```
anthropic>=0.18.0 # Claude API
websockets>=12.0 # Real-time chat
```
**Frontend:**
```
@radix-ui/react-dialog # Modals
@radix-ui/react-tabs # Tab navigation
cmdk # Command palette
framer-motion # Animations
```
### API Keys Required
- `ANTHROPIC_API_KEY`: For Claude Code integration (user provides)
---
## Security Considerations
1. **API Key Storage**: Never store API keys in frontend; use backend proxy
2. **File Access**: Sandbox Claude's file operations to study directories only
3. **Command Execution**: Whitelist allowed commands (no arbitrary shell)
4. **Rate Limiting**: Prevent API abuse through the chat interface
---
## Next Steps
1. Review and approve this plan
2. Prioritize features based on user needs
3. Create GitHub issues for each feature
4. Begin Phase 1 implementation
5. Set up Claude API integration testing
---
*Document Version: 1.0*
*Created: 2024-12-04*
*Author: Claude Code*

View File

@@ -0,0 +1,60 @@
# Backend Integration Plan
## Objective
Implement the backend logic required to support the advanced dashboard features, including study creation, real-time data streaming, 3D mesh conversion, and report generation.
## 1. Enhanced WebSocket Real-Time Streaming
**File**: `atomizer-dashboard/backend/api/websocket/optimization_stream.py`
### Tasks
- [ ] Update `OptimizationFileHandler` to watch for `pareto_front` updates.
- [ ] Update `OptimizationFileHandler` to watch for `optimizer_state` updates.
- [ ] Implement broadcasting logic for new event types: `pareto_front`, `optimizer_state`.
## 2. Study Creation API
**File**: `atomizer-dashboard/backend/api/routes/optimization.py`
### Tasks
- [ ] Implement `POST /api/optimization/studies` endpoint.
- [ ] Add logic to handle multipart/form-data (config + files).
- [ ] Create study directory structure (`1_setup`, `2_results`, etc.).
- [ ] Save uploaded files (`.prt`, `.sim`, `.fem`) to `1_setup/model/`.
- [ ] Save configuration to `1_setup/optimization_config.json`.
## 3. 3D Mesh Visualization API
**File**: `atomizer-dashboard/backend/api/routes/optimization.py` & `optimization_engine/mesh_converter.py`
### Tasks
- [ ] Create `optimization_engine/mesh_converter.py` utility.
- [ ] Implement `convert_to_gltf(bdf_path, op2_path, output_path)` function.
- [ ] Use `pyNastran` to read BDF/OP2.
- [ ] Use `trimesh` (or custom logic) to export GLTF.
- [ ] Implement `POST /api/optimization/studies/{study_id}/convert-mesh` endpoint.
- [ ] Implement `GET /api/optimization/studies/{study_id}/mesh/{filename}` endpoint.
## 4. Report Generation API
**File**: `atomizer-dashboard/backend/api/routes/optimization.py` & `optimization_engine/report_generator.py`
### Tasks
- [ ] Create `optimization_engine/report_generator.py` utility.
- [ ] Implement `generate_report(study_id, format, include_llm)` function.
- [ ] Use `markdown` and `weasyprint` (optional) for rendering.
- [ ] Implement `POST /api/optimization/studies/{study_id}/generate-report` endpoint.
- [ ] Implement `GET /api/optimization/studies/{study_id}/reports/{filename}` endpoint.
## 5. Dependencies
**File**: `atomizer-dashboard/backend/requirements.txt`
### Tasks
- [ ] Add `python-multipart` (for file uploads).
- [ ] Add `pyNastran` (for mesh conversion).
- [ ] Add `trimesh` (optional, for GLTF export).
- [ ] Add `markdown` (for report generation).
- [ ] Add `weasyprint` (optional, for PDF generation).
## Execution Order
1. **Dependencies**: Update `requirements.txt` and install packages.
2. **Study Creation**: Implement the POST endpoint to enable the Configurator.
3. **WebSocket**: Enhance the stream to support advanced visualizations.
4. **3D Pipeline**: Build the mesh converter and API endpoints.
5. **Reporting**: Build the report generator and API endpoints.

View File

@@ -0,0 +1,95 @@
# Advanced Dashboard Enhancement Plan
## Objective
Elevate the Atomizer Dashboard to a "Gemini 3.0 level" experience, focusing on scientific rigor, advanced visualization, and deep integration with the optimization engine. This plan addresses the user's request for a "WAY better" implementation based on the initial master prompt.
## 1. Advanced Visualization Suite (Phase 3 Enhancements)
**Goal**: Replace basic charts with state-of-the-art scientific visualizations.
### 1.1 Parallel Coordinates Plot
- **Library**: Recharts (custom implementation) or D3.js wrapped in React.
- **Features**:
- Visualize high-dimensional relationships between design variables and objectives.
- Interactive brushing/filtering to isolate high-performing designs.
- Color coding by objective value (e.g., mass or stress).
### 1.2 Hypervolume Evolution
- **Goal**: Track the progress of multi-objective optimization.
- **Implementation**:
- Calculate hypervolume metric for each generation/batch.
- Plot evolution over time to show convergence speed and quality.
### 1.3 Pareto Front Evolution
- **Goal**: Visualize the trade-off surface between conflicting objectives.
- **Implementation**:
- 2D/3D scatter plot of objectives.
- Animation slider to show how the front evolves over trials.
- Highlight the "current best" non-dominated solutions.
### 1.4 Parameter Correlation Matrix
- **Goal**: Identify relationships between variables.
- **Implementation**:
- Heatmap showing Pearson/Spearman correlation coefficients.
- Helps users understand which variables drive performance.
## 2. Iteration Analysis & 3D Viewer (Phase 4)
**Goal**: Deep dive into individual trial results with 3D context.
### 2.1 Advanced Trial Table
- **Features**:
- Sortable, filterable columns for all variables and objectives.
- "Compare" mode: Select 2-3 trials to view side-by-side.
- Status indicators with detailed tooltips (e.g., pruning reasons).
### 2.2 3D Mesh Viewer (Three.js)
- **Integration**:
- Load `.obj` or `.gltf` files converted from Nastran `.bdf` or `.op2`.
- **Color Mapping**: Overlay stress/displacement results on the mesh.
- **Controls**: Orbit, zoom, pan, section cuts.
- **Comparison**: Split-screen view for comparing baseline vs. optimized geometry.
## 3. Report Generation (Phase 5)
**Goal**: Automated, publication-ready reporting.
### 3.1 Dynamic Report Builder
- **Features**:
- Markdown-based editor with live preview.
- Drag-and-drop charts from the dashboard into the report.
- LLM integration: "Explain this convergence plot" -> Generates text.
### 3.2 Export Options
- **Formats**: PDF (via `react-to-print` or server-side generation), HTML, Markdown.
- **Content**: Includes high-res charts, tables, and 3D snapshots.
## 4. UI/UX Polish (Scientific Theme)
**Goal**: Professional, "Dark Mode" scientific aesthetic.
- **Typography**: Use a monospaced font for data (e.g., JetBrains Mono, Fira Code) and a clean sans-serif for UI (Inter).
- **Color Palette**:
- Background: `#0a0a0a` (Deep black/gray).
- Accents: Neon cyan/blue for data, muted gray for UI.
- Status: Traffic light colors (Green/Yellow/Red) but desaturated/neon.
- **Layout**:
- Collapsible sidebars for maximum data visibility.
- "Zen Mode" for focusing on specific visualizations.
- Dense data display (compact rows, small fonts) for information density.
## Implementation Roadmap
1. **Step 1: Advanced Visualizations**
- Implement Parallel Coordinates.
- Implement Pareto Front Plot.
- Enhance Convergence Plot with confidence intervals (if available).
2. **Step 2: Iteration Analysis**
- Build the advanced data table with sorting/filtering.
- Create the "Compare Trials" view.
3. **Step 3: 3D Viewer Foundation**
- Set up Three.js canvas.
- Implement basic mesh loading (placeholder geometry first).
- Add color mapping logic.
4. **Step 4: Reporting & Polish**
- Build the report editor.
- Apply the strict "Scientific Dark" theme globally.