feat: Add LLM-native development roadmap and reorganize documentation
- Add DEVELOPMENT_ROADMAP.md with 7-phase plan for LLM-driven optimization - Phase 1: Plugin system with lifecycle hooks - Phase 2: Natural language configuration interface - Phase 3: Dynamic code generation for custom objectives - Phase 4: Intelligent analysis and decision support - Phase 5: Automated HTML/PDF reporting - Phase 6: NX MCP server integration - Phase 7: Self-improving feature registry - Update README.md to reflect LLM-native philosophy - Emphasize natural language workflows - Link to development roadmap - Update architecture diagrams - Add future capability examples - Reorganize documentation structure - Move old dev docs to docs/archive/ - Clean up root directory - Preserve all working optimization engine code This sets the foundation for transforming Atomizer into an AI-powered engineering assistant that can autonomously configure optimizations, generate custom analysis code, and provide intelligent recommendations.
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# Atomizer
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> Advanced optimization platform for Siemens NX Simcenter with LLM-powered configuration
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[](https://www.python.org/downloads/)
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[](LICENSE)
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[](https://github.com)
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## Overview
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Atomizer is a next-generation optimization framework for Siemens NX that combines:
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- **LLM-Driven Configuration**: Use natural language to set up complex optimizations
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- **Advanced Algorithms**: Optuna-powered TPE, Gaussian Process surrogates, multi-fidelity optimization
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- **Real-Time Monitoring**: Interactive dashboards with live updates
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- **Flexible Architecture**: Pluggable result extractors for any FEA analysis type
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- **MCP Integration**: Extensible via Model Context Protocol
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## Architecture
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```
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┌─────────────────────────────────────────────────────────┐
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│ UI Layer │
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│ Web Dashboard (React) + LLM Chat Interface (MCP) │
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└─────────────────────────────────────────────────────────┘
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↕
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┌─────────────────────────────────────────────────────────┐
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│ MCP Server │
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│ - Model Discovery - Config Builder │
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│ - Optimizer Control - Result Analyzer │
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└─────────────────────────────────────────────────────────┘
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↕
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┌─────────────────────────────────────────────────────────┐
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│ Execution Layer │
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│ NX Core (NXOpen) + Optuna Engine + Custom Scripts │
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└─────────────────────────────────────────────────────────┘
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```
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## Quick Start
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### Prerequisites
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- **Siemens NX 2412** with NX Nastran solver
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- **Python 3.10+** (recommend Anaconda)
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- **Node.js 18+** (for dashboard frontend)
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### Installation
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1. **Clone the repository**:
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```bash
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git clone https://github.com/Anto01/Atomizer.git
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cd Atomizer
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```
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2. **Create Python environment**:
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```bash
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conda create -n atomizer python=3.10
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conda activate atomizer
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```
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3. **Install dependencies**:
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```bash
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pip install -e .
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# For development tools:
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pip install -e ".[dev]"
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# For MCP server:
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pip install -e ".[mcp]"
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```
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4. **Configure NX path** (edit `config/nx_config.json`):
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```json
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{
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"nx_executable": "C:/Program Files/Siemens/NX2412/NXBIN/ugraf.exe",
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"python_env": "C:/Users/YourName/anaconda3/envs/atomizer/python.exe"
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}
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```
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### Basic Usage
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#### 1. Conversational Setup (via MCP)
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```
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You: My FEA is in C:\Projects\Bracket\analysis.sim, please import its features.
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AI: I've analyzed your model:
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- Solution: Static Analysis (NX Nastran)
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- Expressions: wall_thickness (5mm), hole_diameter (10mm)
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- Mesh: 8234 nodes, 4521 elements
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Which parameters would you like to optimize?
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You: Optimize wall_thickness and hole_diameter to minimize max stress while keeping mass low.
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AI: Configuration created! Ready to start optimization with 100 iterations.
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Would you like to review the config or start now?
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You: Start it!
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AI: Optimization launched! 🚀
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Dashboard: http://localhost:8080/dashboard
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```
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#### 2. Manual Configuration (JSON)
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Create `optimization_config.json`:
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```json
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{
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"design_variables": {
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"wall_thickness": {
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"low": 3.0,
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"high": 8.0,
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"enabled": true
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}
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},
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"objectives": {
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"metrics": {
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"max_stress": {
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"weight": 10,
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"target": 200,
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"extractor": "nastran_stress"
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}
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}
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},
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"nx_settings": {
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"sim_path": "C:/Projects/Bracket/analysis.sim",
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"solution_name": "Solution 1"
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}
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}
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```
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Run optimization:
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```bash
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python -m optimization_engine.run_optimizer --config optimization_config.json
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```
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## Features
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### ✨ Core Capabilities
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- **Multi-Objective Optimization**: Weighted sum, Pareto front analysis
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- **Smart Sampling**: TPE, Latin Hypercube, Gaussian Process surrogates
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- **Result Extraction**: Nastran (OP2/F06), NX Mass Properties, custom parsers
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- **Crash Recovery**: Automatic resume from interruptions
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- **Parallel Evaluation**: Multi-core FEA solving (coming soon)
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### 📊 Visualization
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- **Real-time progress monitoring**
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- **3D Pareto front plots** (Plotly)
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- **Parameter importance charts**
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- **Convergence history**
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- **FEA result overlays**
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### 🔧 Extensibility
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- **Pluggable result extractors**: Add custom metrics easily
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- **Custom post-processing scripts**: Python integration
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- **MCP tools**: Extend via protocol
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- **NXOpen API access**: Full NX automation
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## Project Structure
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```
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Atomizer/
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├── mcp_server/ # MCP server implementation
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│ ├── tools/ # MCP tool definitions
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│ ├── schemas/ # JSON schemas for validation
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│ └── prompts/ # LLM system prompts
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├── optimization_engine/ # Core optimization logic
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│ ├── result_extractors/ # Pluggable metric extractors
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│ ├── multi_optimizer.py # Optuna integration
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│ ├── config_loader.py # Configuration parser
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│ └── history_manager.py # CSV/SQLite persistence
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├── nx_journals/ # NXOpen Python scripts
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│ ├── update_and_solve.py # CAD update + solver
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│ ├── post_process.py # Result extraction
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│ └── utils/ # Helper functions
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├── dashboard/ # Web UI
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│ ├── frontend/ # React app
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│ └── backend/ # FastAPI server
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├── tests/ # Unit tests
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├── examples/ # Example projects
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└── docs/ # Documentation
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```
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## Configuration Schema
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See [docs/configuration.md](docs/configuration.md) for full schema documentation.
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**Key sections**:
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- `design_variables`: Parameters to optimize
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- `objectives`: Metrics to minimize/maximize
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- `nx_settings`: NX/FEA solver configuration
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- `optimization`: Optuna sampler settings
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- `post_processing`: Result extraction pipelines
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## Development
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### Running Tests
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```bash
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pytest
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```
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### Code Formatting
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```bash
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black .
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ruff check .
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```
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### Building Documentation
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```bash
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cd docs
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mkdocs build
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```
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## Roadmap
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- [x] MCP server foundation
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- [x] Basic optimization engine
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- [ ] NXOpen integration
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- [ ] Web dashboard
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- [ ] Multi-fidelity optimization
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- [ ] Parallel evaluations
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- [ ] Sensitivity analysis tools
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- [ ] Export to engineering reports
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## Contributing
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This is a private repository. Contact [contact@atomaste.com](mailto:contact@atomaste.com) for access.
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## License
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Proprietary - Atomaste © 2025
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## Support
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- **Documentation**: [docs/](docs/)
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- **Examples**: [examples/](examples/)
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- **Issues**: GitHub Issues (private repository)
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- **Email**: support@atomaste.com
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## Resources
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### NXOpen References
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- **Official API Docs**: [Siemens NXOpen .NET Documentation](https://docs.sw.siemens.com/en-US/doc/209349590/)
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- **NXOpenTSE**: [The Scripting Engineer's Documentation](https://nxopentsedocumentation.thescriptingengineer.com/) (reference for patterns and best practices)
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- **Our Guide**: [NXOpen Resources](docs/NXOPEN_RESOURCES.md)
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### Optimization
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- **Optuna Documentation**: [optuna.readthedocs.io](https://optuna.readthedocs.io/)
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- **pyNastran**: [github.com/SteveDoyle2/pyNastran](https://github.com/SteveDoyle2/pyNastran)
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---
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**Built with ❤️ by Atomaste** | Powered by Optuna, NXOpen, and Claude
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