MM-neural-adjoint 0.1.11

A neural adjoint method implementation

MM-Neural-Adjoint

A Python package implementing neural adjoint methods, specifically designed for predicting the geometries of metamaterials. This implementation is based on the work from BDIMNNA (Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method), published in NeurIPS 2020 by Simiao Ren, Willie J. Padilla and Jordan Malof.

About

This package focuses on the Neural Adjoint (NA) method for inverse design of metamaterials. It provides a streamlined implementation specifically optimized for metamaterial geometry prediction tasks, building upon the benchmarking work done in the original BDIMNNA repository.

Installation

This package supports different hardware configurations including CPU, Apple Silicon (M1/M2), and NVIDIA GPUs. Choose the appropriate installation method based on your hardware:

Basic Installation (CPU/Apple Silicon)

pip install -e .

For NVIDIA GPU Users

If you have an NVIDIA GPU, you can install with CUDA support:

# For CUDA 11.8
pip install -e .[cuda]

# For CUDA 12.1
pip install -e .[cuda12]

Development Installation

For contributors and developers:

# Clone the repository
git clone https://github.com/yourusername/MM-Neural-Adjoint.git
cd MM-Neural-Adjoint

# Install in editable mode with all dependencies
pip install -e .

Hardware Support

• CPU: Supported on all platforms
• Apple Silicon (M1/M2): GPU acceleration via MPS backend (included in default installation)
• NVIDIA GPUs: CUDA support available through specific installation options

Usage

After installation, you can import and use the package in your Python code:

import mm_neural_adjoint

Examples

The package includes several example notebooks and scripts to help you get started with metamaterial design using the Neural Adjoint method. You can find these in the examples/ directory:

• examples/example1.ipynb: Introduction to basic package functionality

To run the examples:

cd examples
jupyter notebook

## Requirements

- Python >= 3.7
- PyTorch >= 2.6.0
- NumPy >= 2.2.4
- Pandas >= 2.2.3
- tqdm >= 4.67.1
- MLflow >= 2.21.3
- scikit-learn >= 1.4.0

## Acknowledgments

This package is based on the Neural Adjoint implementation from the [BDIMNNA repository](https://github.com/BensonRen/BDIMNNA) by Benson Ren et al. We thank the original authors for their foundational work in developing and benchmarking the Neural Adjoint method.