fdtdx 0.6.0
A JAX framework for the finite-difference time-domain (FDTD) method for solving Maxwell's equations with a focus on inverse design of photonic devices.
Requires Python: >=3.11


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# FDTDX: Electromagnetic Simulations in JAX

FDTDX is an efficient open-source Python package for the simulation and design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Built on JAX, it provides native GPU support and automatic differentiation capabilities, making it ideal for large-scale design tasks.

## Key Features
The key features differentiating FDTDX from other simulation software packages like Meep (which is also great!) are the following:

- **High Performance**: GPU-accelerated FDTD simulations with multi-GPU scaling capabilities
- **Memory Efficient**: Leverages time-reversibility in Maxwell's equations for efficient gradient computation
- **Automatic Differentiation**: Built-in gradient-based optimization for complex 3D structures
- **User-Friendly API**: Intuitive positioning and sizing of objects in absolute or relative coordinates
- **Large-Scale Design**: Capable of handling simulations with billions of grid cells
- **Open Source**: Freely available for research, development and commercial use.

## Documentation

Visit our [documentation](https://fdtdx.readthedocs.io/en/latest/) for:
- Detailed API reference
- Tutorial guides
- Best practices

Also check out our [whitepaper](https://arxiv.org/abs/2412.12360) for some examples and a comparison to other popular FDTD-frameworks.

## Installation

Install FDTDX using pip:

```bash
pip install fdtdx  # Basic CPU-Installation
pip install fdtdx[cuda12]  # GPU-Acceleration (Highly Recommended!)
pip install fdtdx[rocm]   # AMD-GPU (only python<=3.12)
```

For development installation, see the contributing guidelines!

## Multi-GPU

```bash
# The following lines often lead to better memory usage in JAX
# when using multiple GPU.
export XLA_PYTHON_CLIENT_ALLOCATOR="platform"
export XLA_PYTHON_CLIENT_PREALLOCATE="false"
export NCCL_LL128_BUFFSIZE="-2"
export NCCL_LL_BUFFSIZE="-2"
export NCCL_PROTO="SIMPLE,LL,LL128"
```

## Citation
If you find this repository helpful for you work, please consider citing:
```
@article{Mahlau2026,
  doi = {10.21105/joss.08912},
  url = {https://doi.org/10.21105/joss.08912},
  year = {2026},
  publisher = {The Open Journal},
  volume = {11},
  number = {117},
  pages = {8912},
  author = {Mahlau, Yannik and Schubert, Frederik and Berg, Lukas and Rosenhahn, Bodo},
  title = {FDTDX: High-Performance Open-Source FDTD Simulation with Automatic Differentiation},
  journal = {Journal of Open Source Software}
}
```

## Acknowedgement
This project was developed at the [Institute of Information Processing](https://www.tnt.uni-hannover.de/) at Leibniz University Hannover, Germany and sponsored by the cluster of excellence [PhoenixD](https://www.phoenixd.uni-hannover.de/en/) (Photonics, Optics, Engineering, Innovation across Disciplines).