Meent is an Electromagnetic(EM) simulation package with Python, composed of three main parts:
- Modeling
- EM simulation
- Optimization
Meent provides three libraries as a backend:
- NumPy
- The fundamental package for scientific computing with Python
- Easy and lean to use
- JAX
- Autograd and XLA, brought together for high-performance machine learning research.
- PyTorch
- A Python package that provides two high-level features: Tensor computation with strong GPU acceleration and Deep neural networks built on a tape-based autograd system
| Numpy | JAX | PyTorch | Description | |
|---|---|---|---|---|
| 64bit support | O | O | O | Default for scientific computing |
| 32bit support | O | O | O | 32bit (float32 and complex64) data type operation* |
| GPU support | X | O | O | except Eigendecomposition** |
| TPU support* | X | X | X | Currently there is no workaround to do 32 bit eigendecomposition on TPU |
| AD support | X | O | O | Automatic Differentiation (Back Propagation) |
| Parallelization | X | O | X | JAX pmap function |
*In 32bit operation, operations on numbers of 8>= digit difference fail without warning or error.
Use only when you do understand what you are doing.
**As of now(2023.03.19), GPU-native Eigendecomposition is not implemented in JAX and PyTorch.
It's enforced to run on CPUs and send back to GPUs.
Numpy is simple and light to use. Suggested as a baseline with small ~ medium scale optics problem.
JAX and PyTorch is recommended for cases having large scale or optimization part.
If you want parallelized computing with multiple devices(e.g., GPUs), JAX is ready for that.
But since JAX does jit compilation, it takes much time at the first run.
pip install meentJAX and PyTorch is needed for advanced utilization.
import meent
# backend 0 = Numpy
# backend 1 = JAX
# backend 2 = PyTorch
backend = 1
mee = meent.call_mee(backend=backend, ...)Jupyter notebooks are prepared in tutorials to give a brief introduction.
Comprehensive examples of computational optics with Meent can be found in examples folder.
To cite this repository:
@software{Kim_Meent_Electromagnetic_simulation,
author = {Kim, Yongha and Kim, Sanmun and Lee, Jinmyoung and Jung, Anthony W. and Kim, Seolho},
license = {MIT},
title = {{Meent:Electromagnetic simulation & optimization package in Python}},
url = {https://github.com/kc-ml2/meent}
}
Will be updated.
[1] https://opg.optica.org/josa/abstract.cfm?uri=josa-71-7-811, Rigorous coupled-wave analysis of planar-grating diffraction
[2] https://opg.optica.org/josaa/abstract.cfm?uri=josaa-12-5-1068
[3] https://opg.optica.org/josaa/abstract.cfm?uri=josaa-12-5-1077
[4] https://opg.optica.org/josaa/abstract.cfm?uri=josaa-13-5-1019
[5] https://opg.optica.org/josaa/abstract.cfm?uri=josaa-13-4-779
[6] https://opg.optica.org/josaa/abstract.cfm?uri=josaa-13-9-1870
[7] https://empossible.net/emp5337/
[8] https://github.com/zhaonat/Rigorous-Coupled-Wave-Analysis (see also our fork: https://github.com/yonghakim/zhaonat-rcwa)
[9] https://arxiv.org/abs/2101.00901