docs: Add comprehensive architecture overview with Mermaid diagrams
Complete visual guide to understanding Atomizer's architecture including: - Session lifecycle (startup, active, closing) - Protocol Operating System (4-layer architecture) - Learning Atomizer Core (LAC) data flow - Task classification and routing - AVERVS execution framework - Optimization flow with extractors - Knowledge accumulation over time - File structure reference Includes 15+ Mermaid diagrams for visual learning. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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docs/07_DEVELOPMENT/ATOMIZER_ARCHITECTURE_OVERVIEW.md
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# Atomizer Architecture Overview
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**Version**: 1.0
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**Last Updated**: 2025-12-11
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**Purpose**: Comprehensive guide to understanding how Atomizer works - from session management to learning systems.
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---
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## Table of Contents
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1. [What is Atomizer?](#1-what-is-atomizer)
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2. [The Big Picture](#2-the-big-picture)
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3. [Session Lifecycle](#3-session-lifecycle)
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4. [Protocol Operating System (POS)](#4-protocol-operating-system-pos)
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5. [Learning Atomizer Core (LAC)](#5-learning-atomizer-core-lac)
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6. [Task Classification & Routing](#6-task-classification--routing)
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7. [Execution Framework (AVERVS)](#7-execution-framework-avervs)
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8. [Optimization Flow](#8-optimization-flow)
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9. [Knowledge Accumulation](#9-knowledge-accumulation)
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10. [File Structure Reference](#10-file-structure-reference)
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---
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## 1. What is Atomizer?
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Atomizer is an **LLM-first FEA optimization framework**. Instead of clicking through complex GUI menus, engineers describe their optimization goals in natural language, and an AI assistant (Claude) configures, runs, and analyzes the optimization.
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```mermaid
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graph LR
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subgraph Traditional["Traditional Workflow"]
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A1[Engineer] -->|clicks| B1[NX GUI]
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B1 -->|manual setup| C1[Optuna Config]
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C1 -->|run| D1[Results]
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end
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subgraph Atomizer["Atomizer Workflow"]
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A2[Engineer] -->|"'Minimize mass while keeping stress < 250 MPa'"| B2[Atomizer Claude]
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B2 -->|auto-configures| C2[NX + Optuna]
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C2 -->|run| D2[Results + Insights]
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D2 -->|learns| B2
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end
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style Atomizer fill:#e1f5fe
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```
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**Core Philosophy**: "Talk, don't click."
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---
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## 2. The Big Picture
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```mermaid
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graph TB
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subgraph User["👤 Engineer"]
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U1[Natural Language Request]
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end
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subgraph Claude["🤖 Atomizer Claude"]
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C1[Session Manager]
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C2[Protocol Router]
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C3[Task Executor]
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C4[Learning System]
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end
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subgraph POS["📚 Protocol Operating System"]
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P1[Bootstrap Layer]
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P2[Operations Layer]
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P3[System Layer]
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P4[Extensions Layer]
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end
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subgraph LAC["🧠 Learning Atomizer Core"]
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L1[Optimization Memory]
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L2[Session Insights]
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L3[Skill Evolution]
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end
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subgraph Engine["⚙️ Optimization Engine"]
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E1[NX Open API]
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E2[Nastran Solver]
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E3[Optuna Optimizer]
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E4[Extractors]
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end
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U1 --> C1
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C1 --> C2
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C2 --> P1
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P1 --> P2
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P2 --> P3
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C2 --> C3
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C3 --> Engine
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C3 --> C4
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C4 --> LAC
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LAC -.->|prior knowledge| C2
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style Claude fill:#fff3e0
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style LAC fill:#e8f5e9
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style POS fill:#e3f2fd
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```
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---
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## 3. Session Lifecycle
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Every Claude session follows a structured lifecycle:
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```mermaid
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stateDiagram-v2
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[*] --> Startup: New Session
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state Startup {
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[*] --> EnvCheck: Check conda environment
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EnvCheck --> LoadContext: Load CLAUDE.md + Bootstrap
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LoadContext --> QueryLAC: Query prior knowledge
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QueryLAC --> DetectStudy: Check for active study
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DetectStudy --> [*]
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}
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Startup --> Active: Ready
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state Active {
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[*] --> Classify: Receive request
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Classify --> Route: Determine task type
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Route --> Execute: Load protocols & execute
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Execute --> Record: Record learnings
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Record --> [*]: Ready for next
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}
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Active --> Closing: Session ending
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state Closing {
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[*] --> SaveWork: Verify work saved
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SaveWork --> RecordLAC: Record insights to LAC
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RecordLAC --> RecordOutcome: Record optimization outcomes
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RecordOutcome --> Summarize: Summarize for user
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Summarize --> [*]
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}
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Closing --> [*]: Session complete
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```
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### Startup Checklist
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| Step | Action | Purpose |
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|------|--------|---------|
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| 1 | Environment check | Ensure `atomizer` conda env active |
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| 2 | Load context | Read CLAUDE.md, Bootstrap |
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| 3 | Query LAC | Get relevant prior learnings |
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| 4 | Detect study | Check for active study context |
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### Closing Checklist
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| Step | Action | Purpose |
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|------|--------|---------|
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| 1 | Save work | Commit files, validate configs |
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| 2 | Record learnings | Store failures, successes, workarounds |
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| 3 | Record outcomes | Store optimization results |
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| 4 | Summarize | Provide next steps to user |
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---
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## 4. Protocol Operating System (POS)
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The POS is Atomizer's documentation architecture - a layered system that provides the right context at the right time.
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```mermaid
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graph TB
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subgraph Layer1["Layer 1: Bootstrap (Always Loaded)"]
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B1[00_BOOTSTRAP.md<br/>Task classification & routing]
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B2[01_CHEATSHEET.md<br/>Quick reference]
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B3[02_CONTEXT_LOADER.md<br/>What to load when]
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end
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subgraph Layer2["Layer 2: Operations (Per Task)"]
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O1[OP_01 Create Study]
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O2[OP_02 Run Optimization]
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O3[OP_03 Monitor Progress]
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O4[OP_04 Analyze Results]
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O5[OP_05 Export Data]
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O6[OP_06 Troubleshoot]
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end
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subgraph Layer3["Layer 3: System (Technical Specs)"]
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S1[SYS_10 IMSO<br/>Adaptive sampling]
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S2[SYS_11 Multi-Objective<br/>Pareto optimization]
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S3[SYS_12 Extractors<br/>Physics extraction]
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S4[SYS_13 Dashboard<br/>Real-time monitoring]
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S5[SYS_14 Neural<br/>Surrogate acceleration]
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S6[SYS_15 Method Selector<br/>Algorithm selection]
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end
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subgraph Layer4["Layer 4: Extensions (Power Users)"]
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E1[EXT_01 Create Extractor]
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E2[EXT_02 Create Hook]
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E3[EXT_03 Create Protocol]
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E4[EXT_04 Create Skill]
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end
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Layer1 --> Layer2
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Layer2 --> Layer3
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Layer3 --> Layer4
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style Layer1 fill:#e3f2fd
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style Layer2 fill:#e8f5e9
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style Layer3 fill:#fff3e0
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style Layer4 fill:#fce4ec
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```
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### Loading Rules
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```mermaid
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flowchart TD
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A[User Request] --> B{Classify Task}
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B -->|Create| C1[Load: study-creation-core.md]
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B -->|Run| C2[Load: OP_02_RUN_OPTIMIZATION.md]
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B -->|Monitor| C3[Load: OP_03_MONITOR_PROGRESS.md]
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B -->|Analyze| C4[Load: OP_04_ANALYZE_RESULTS.md]
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B -->|Debug| C5[Load: OP_06_TROUBLESHOOT.md]
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B -->|Extend| C6{Check Privilege}
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C1 --> D1{Signals?}
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D1 -->|Mirror/Zernike| E1[+ zernike-optimization.md]
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D1 -->|Neural/50+ trials| E2[+ SYS_14_NEURAL.md]
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D1 -->|Multi-objective| E3[+ SYS_11_MULTI.md]
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C6 -->|power_user| F1[Load: EXT_01 or EXT_02]
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C6 -->|admin| F2[Load: Any EXT_*]
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C6 -->|user| F3[Deny - explain]
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```
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---
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## 5. Learning Atomizer Core (LAC)
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LAC is Atomizer's persistent memory - it learns from every session.
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```mermaid
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graph TB
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subgraph LAC["🧠 Learning Atomizer Core"]
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subgraph OM["Optimization Memory"]
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OM1[bracket.jsonl]
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OM2[beam.jsonl]
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OM3[mirror.jsonl]
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end
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subgraph SI["Session Insights"]
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SI1[failure.jsonl<br/>What went wrong & why]
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SI2[success_pattern.jsonl<br/>What worked well]
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SI3[workaround.jsonl<br/>Known fixes]
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SI4[user_preference.jsonl<br/>User preferences]
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end
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subgraph SE["Skill Evolution"]
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SE1[suggested_updates.jsonl<br/>Protocol improvements]
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end
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end
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subgraph Session["Current Session"]
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S1[Query prior knowledge]
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S2[Execute tasks]
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S3[Record learnings]
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end
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S1 -->|read| LAC
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S3 -->|write| LAC
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LAC -.->|informs| S2
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style LAC fill:#e8f5e9
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```
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### LAC Data Flow
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```mermaid
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sequenceDiagram
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participant U as User
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participant C as Claude
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participant LAC as LAC
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participant Opt as Optimizer
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Note over C,LAC: Session Start
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C->>LAC: query_similar_optimizations("bracket", ["mass"])
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LAC-->>C: Similar studies: TPE worked 85% of time
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C->>LAC: get_relevant_insights("bracket optimization")
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LAC-->>C: Insight: "20 startup trials improves convergence"
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Note over U,Opt: During Session
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U->>C: "Optimize my bracket for mass"
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C->>C: Apply prior knowledge
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C->>Opt: Configure with TPE, 20 startup trials
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Opt-->>C: Optimization complete
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Note over C,LAC: Discovery
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C->>C: Found: CMA-ES faster for this case
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C->>LAC: record_insight("success_pattern", "CMA-ES faster for simple brackets")
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Note over C,LAC: Session End
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C->>LAC: record_optimization_outcome(study="bracket_v4", converged=true, ...)
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```
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### What LAC Stores
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| Category | Examples | Used For |
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|----------|----------|----------|
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| **Optimization Memory** | Method used, convergence, trials | Recommending methods for similar problems |
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| **Failures** | "CMA-ES failed on discrete targets" | Avoiding repeat mistakes |
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| **Success Patterns** | "TPE with 20 startup trials converges faster" | Applying proven techniques |
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| **Workarounds** | "Load _i.prt before UpdateFemodel()" | Fixing known issues |
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| **Protocol Updates** | "SYS_15 should mention CMA-ES limitation" | Improving documentation |
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---
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## 6. Task Classification & Routing
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```mermaid
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flowchart TD
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A[User Request] --> B{Contains keywords?}
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B -->|"new, create, set up, optimize"| C1[CREATE]
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B -->|"run, start, execute, begin"| C2[RUN]
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B -->|"status, progress, check, trials"| C3[MONITOR]
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B -->|"results, best, compare, pareto"| C4[ANALYZE]
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B -->|"error, failed, not working, help"| C5[DEBUG]
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B -->|"what is, how does, explain"| C6[EXPLAIN]
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B -->|"create extractor, add hook"| C7[EXTEND]
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C1 --> D1[OP_01 + study-creation-core]
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C2 --> D2[OP_02]
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C3 --> D3[OP_03]
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C4 --> D4[OP_04]
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C5 --> D5[OP_06]
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C6 --> D6[Relevant SYS_*]
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C7 --> D7{Privilege?}
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D7 -->|user| E1[Explain limitation]
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D7 -->|power_user+| E2[EXT_01 or EXT_02]
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style C1 fill:#c8e6c9
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style C2 fill:#bbdefb
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style C3 fill:#fff9c4
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style C4 fill:#d1c4e9
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style C5 fill:#ffccbc
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style C6 fill:#b2ebf2
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style C7 fill:#f8bbd9
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```
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---
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## 7. Execution Framework (AVERVS)
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Every task follows the AVERVS pattern:
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```mermaid
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graph LR
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A[Announce] --> V1[Validate]
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V1 --> E[Execute]
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E --> R[Report]
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R --> V2[Verify]
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V2 --> S[Suggest]
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style A fill:#e3f2fd
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style V1 fill:#fff3e0
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style E fill:#e8f5e9
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style R fill:#fce4ec
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style V2 fill:#f3e5f5
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style S fill:#e0f2f1
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```
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### AVERVS in Action
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```mermaid
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sequenceDiagram
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participant U as User
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participant C as Claude
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participant NX as NX/Solver
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U->>C: "Create a study for my bracket"
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Note over C: A - Announce
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C->>U: "I'm going to analyze your model to discover expressions and setup"
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Note over C: V - Validate
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C->>C: Check: .prt exists? .sim exists? _i.prt present?
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C->>U: "✓ All required files present"
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Note over C: E - Execute
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C->>NX: Run introspection script
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NX-->>C: Expressions, constraints, solutions
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Note over C: R - Report
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C->>U: "Found 12 expressions, 3 are design variable candidates"
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Note over C: V - Verify
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C->>C: Validate generated config
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C->>U: "✓ Config validation passed"
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Note over C: S - Suggest
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C->>U: "Ready to run. Want me to:<br/>1. Start optimization now?<br/>2. Adjust parameters first?"
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```
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---
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## 8. Optimization Flow
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```mermaid
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flowchart TB
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subgraph Setup["1. Setup Phase"]
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A1[User describes goal] --> A2[Claude analyzes model]
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A2 --> A3[Query LAC for similar studies]
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A3 --> A4[Generate optimization_config.json]
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A4 --> A5[Create run_optimization.py]
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end
|
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subgraph Run["2. Optimization Loop"]
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B1[Optuna suggests parameters] --> B2[Update NX expressions]
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B2 --> B3[Update FEM mesh]
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B3 --> B4[Solve with Nastran]
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B4 --> B5[Extract results via Extractors]
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B5 --> B6[Report to Optuna]
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B6 --> B7{More trials?}
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B7 -->|Yes| B1
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B7 -->|No| C1
|
||||
end
|
||||
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subgraph Analyze["3. Analysis Phase"]
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||||
C1[Load study.db] --> C2[Find best trials]
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||||
C2 --> C3[Generate visualizations]
|
||||
C3 --> C4[Create STUDY_REPORT.md]
|
||||
end
|
||||
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subgraph Learn["4. Learning Phase"]
|
||||
D1[Record outcome to LAC]
|
||||
D2[Record insights discovered]
|
||||
D3[Suggest protocol updates]
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||||
end
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||||
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||||
Setup --> Run
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||||
Run --> Analyze
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||||
Analyze --> Learn
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||||
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||||
style Setup fill:#e3f2fd
|
||||
style Run fill:#e8f5e9
|
||||
style Analyze fill:#fff3e0
|
||||
style Learn fill:#f3e5f5
|
||||
```
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||||
### Extractors
|
||||
|
||||
Extractors bridge FEA results to optimization objectives:
|
||||
|
||||
```mermaid
|
||||
graph LR
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||||
subgraph FEA["FEA Output"]
|
||||
F1[OP2 File]
|
||||
F2[BDF File]
|
||||
F3[NX Part]
|
||||
end
|
||||
|
||||
subgraph Extractors["Extractor Library"]
|
||||
E1[E1: Displacement]
|
||||
E2[E2: Frequency]
|
||||
E3[E3: Stress]
|
||||
E4[E4: Mass BDF]
|
||||
E5[E5: Mass CAD]
|
||||
E8[E8: Zernike WFE]
|
||||
end
|
||||
|
||||
subgraph Output["Optimization Values"]
|
||||
O1[Objective Value]
|
||||
O2[Constraint Value]
|
||||
end
|
||||
|
||||
F1 --> E1
|
||||
F1 --> E2
|
||||
F1 --> E3
|
||||
F2 --> E4
|
||||
F3 --> E5
|
||||
F1 --> E8
|
||||
|
||||
E1 --> O1
|
||||
E2 --> O2
|
||||
E3 --> O2
|
||||
E4 --> O1
|
||||
E5 --> O1
|
||||
E8 --> O1
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 9. Knowledge Accumulation
|
||||
|
||||
Atomizer gets smarter over time:
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
subgraph Sessions["Claude Sessions Over Time"]
|
||||
S1[Session 1<br/>Bracket optimization]
|
||||
S2[Session 2<br/>Beam optimization]
|
||||
S3[Session 3<br/>Mirror optimization]
|
||||
S4[Session N<br/>New optimization]
|
||||
end
|
||||
|
||||
subgraph LAC["LAC Knowledge Base"]
|
||||
K1[Optimization<br/>Patterns]
|
||||
K2[Failure<br/>Solutions]
|
||||
K3[Method<br/>Recommendations]
|
||||
end
|
||||
|
||||
S1 -->|record| LAC
|
||||
S2 -->|record| LAC
|
||||
S3 -->|record| LAC
|
||||
LAC -->|inform| S4
|
||||
|
||||
subgraph Improvement["Continuous Improvement"]
|
||||
I1[Better method selection]
|
||||
I2[Faster convergence]
|
||||
I3[Fewer failures]
|
||||
end
|
||||
|
||||
LAC --> Improvement
|
||||
|
||||
style LAC fill:#e8f5e9
|
||||
style Improvement fill:#fff3e0
|
||||
```
|
||||
|
||||
### Example: Method Selection Improvement
|
||||
|
||||
```mermaid
|
||||
graph LR
|
||||
subgraph Before["Without LAC"]
|
||||
B1[New bracket optimization]
|
||||
B2[Default: TPE]
|
||||
B3[Maybe suboptimal]
|
||||
end
|
||||
|
||||
subgraph After["With LAC"]
|
||||
A1[New bracket optimization]
|
||||
A2[Query LAC:<br/>'bracket mass optimization']
|
||||
A3[LAC returns:<br/>'CMA-ES 30% faster for<br/>simple brackets']
|
||||
A4[Use CMA-ES]
|
||||
A5[Faster convergence]
|
||||
end
|
||||
|
||||
B1 --> B2 --> B3
|
||||
A1 --> A2 --> A3 --> A4 --> A5
|
||||
|
||||
style After fill:#e8f5e9
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 10. File Structure Reference
|
||||
|
||||
```
|
||||
Atomizer/
|
||||
├── CLAUDE.md # 🎯 Main instructions (read first)
|
||||
│
|
||||
├── .claude/
|
||||
│ ├── skills/
|
||||
│ │ ├── 00_BOOTSTRAP.md # Task classification
|
||||
│ │ ├── 01_CHEATSHEET.md # Quick reference
|
||||
│ │ ├── 02_CONTEXT_LOADER.md # What to load when
|
||||
│ │ ├── core/
|
||||
│ │ │ └── study-creation-core.md
|
||||
│ │ └── modules/
|
||||
│ │ ├── learning-atomizer-core.md # LAC documentation
|
||||
│ │ ├── zernike-optimization.md
|
||||
│ │ └── neural-acceleration.md
|
||||
│ └── commands/ # Slash commands
|
||||
│
|
||||
├── knowledge_base/
|
||||
│ ├── lac.py # LAC implementation
|
||||
│ └── lac/ # LAC data storage
|
||||
│ ├── optimization_memory/ # What worked for what
|
||||
│ ├── session_insights/ # Learnings
|
||||
│ └── skill_evolution/ # Protocol updates
|
||||
│
|
||||
├── docs/protocols/
|
||||
│ ├── operations/ # OP_01 - OP_06
|
||||
│ ├── system/ # SYS_10 - SYS_15
|
||||
│ └── extensions/ # EXT_01 - EXT_04
|
||||
│
|
||||
├── optimization_engine/
|
||||
│ ├── extractors/ # Physics extraction
|
||||
│ ├── hooks/ # NX automation
|
||||
│ └── gnn/ # Neural surrogates
|
||||
│
|
||||
└── studies/ # User studies
|
||||
└── {study_name}/
|
||||
├── 1_setup/
|
||||
│ ├── model/ # NX files
|
||||
│ └── optimization_config.json
|
||||
├── 2_results/
|
||||
│ └── study.db # Optuna database
|
||||
└── run_optimization.py
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Quick Reference: The Complete Flow
|
||||
|
||||
```mermaid
|
||||
graph TB
|
||||
subgraph Start["🚀 Session Start"]
|
||||
A1[Load CLAUDE.md]
|
||||
A2[Load Bootstrap]
|
||||
A3[Query LAC]
|
||||
end
|
||||
|
||||
subgraph Work["⚙️ During Session"]
|
||||
B1[Classify request]
|
||||
B2[Load protocols]
|
||||
B3[Execute AVERVS]
|
||||
B4[Record insights]
|
||||
end
|
||||
|
||||
subgraph End["🏁 Session End"]
|
||||
C1[Save work]
|
||||
C2[Record to LAC]
|
||||
C3[Summarize]
|
||||
end
|
||||
|
||||
Start --> Work --> End
|
||||
|
||||
subgraph Legend["Legend"]
|
||||
L1[📚 POS: What to do]
|
||||
L2[🧠 LAC: What we learned]
|
||||
L3[⚡ AVERVS: How to do it]
|
||||
end
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
| Component | Purpose | Key Files |
|
||||
|-----------|---------|-----------|
|
||||
| **CLAUDE.md** | Main instructions | `CLAUDE.md` |
|
||||
| **Bootstrap** | Task routing | `00_BOOTSTRAP.md` |
|
||||
| **POS** | Protocol system | `docs/protocols/` |
|
||||
| **LAC** | Learning system | `knowledge_base/lac.py` |
|
||||
| **AVERVS** | Execution pattern | Embedded in protocols |
|
||||
| **Extractors** | Physics extraction | `optimization_engine/extractors/` |
|
||||
|
||||
**The key insight**: Atomizer is not just an optimization tool - it's a *learning* optimization tool that gets better with every session.
|
||||
|
||||
---
|
||||
|
||||
*Atomizer: Where engineers talk, AI optimizes, and every session makes the next one better.*
|
||||
Reference in New Issue
Block a user