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.
Details on how to install and use EMopt can be found on readthedocs. 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.
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.
Andrew Michaels
Sean Hooten (Topology and AutoDiff methods)
EMOpt is currently released under the BSD-3 license (see LICENSE.md for details)
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.