https://github.com/anstmichaels/emopt

A suite of tools for optimizing the shape and topology of electromagnetic structures.
https://github.com/anstmichaels/emopt

Last synced: 2 months ago
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A suite of tools for optimizing the shape and topology of electromagnetic structures.

• Host: GitHub
• URL: https://github.com/anstmichaels/emopt
• Owner: anstmichaels
• License: bsd-3-clause
• Created: 2017-11-03T22:23:29.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-03-08T03:41:32.000Z (4 months ago)
• Last Synced: 2024-03-08T05:42:06.046Z (4 months ago)
• Language: Python
• Size: 1.49 MB
• Stars: 85
• Watchers: 9
• Forks: 38
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG
  • License: LICENSE

Lists

• awesome_photonics - emopt FDTD

README

        
# EMopt

A toolkit for shape (and topology) optimization of 2D and 3D electromagnetic

structures. 

EMopt offers a suite of tools for simulating and optimizing electromagnetic

structures. It includes 2D and 3D finite difference frequency domain solvers,

1D and 2D mode solvers, a flexible and *easily extensible* adjoint method

implementation, and a simple wrapper around scipy.minimize. Out of the box, it

provides just about everything needed to apply cutting-edge inverse design

techniques to your electromagnetic devices.

A key emphasis of EMopt's is shape optimization. Using boundary smoothing

techniques, EMopt allows you to compute sensitivities (i.e. gradient of a

figure of merit with respect to design variables which define an

electromagnetic device's shape) with very high accuracy. This allows you to

easily take adavantage of powerful minimization techniques in order to optimize

your electromagnetic device.

## Documentation

Details on how to install and use EMopt can be found

[on readthedocs](https://emopt.readthedocs.io/en/latest/). Check this link

periodically as the documentation is constantly being improved and examples

added.

New: please see the new `mamba`-based install script `setup.sh` for

streamlined installation.

## Free-Form Topology and AutoDiff-Enhanced Feature-Mapping Approaches

New optional experimental modules for topology optimization and automatic 

differentiation enhanced feature-mapping approaches are implemented in 

emopt/experimental, with corresponding examples in examples/experimental. 

The AutoDiff methods can result in large improvements in optimization speed for 

designs with variables that parameterize global geometric features. Please see 

our preprint below and examples for correct usage. Note: Requires PyTorch 

installation. These features are still in development.

## Authors

Andrew Michaels 

Sean Hooten (Topology and AutoDiff methods)

## License

EMOpt is currently released under the BSD-3 license (see LICENSE.md for details)

## References

The methods employed by EMopt are described in:

Andrew Michaels and Eli Yablonovitch, "Leveraging continuous material averaging for inverse electromagnetic design," Opt. Express 26, 31717-31737 (2018)

An example of applying these methods to real design problems can be found in:

Andrew Michaels and Eli Yablonovitch, "Inverse design of near unity efficiency perfectly vertical grating couplers," Opt. Express 26, 4766-4779 (2018)

Shape optimization feature-mapping methods accelerated by automatic differentiation:

S. Hooten, P. Sun, L. Gantz, M. Fiorentino, R. Beausoleil, T. Van Vaerenbergh, "Automatic Differentiation Accelerated Shape Optimization Approaches to Photonic Inverse Design on Rectilinear Simulation Grids." arXiv [cs.CE], 2311.05646 (2023). Link [here](https://arxiv.org/abs/2311.05646).