TorchGDM: A GPU-Accelerated Python Toolkit for Multi-Scale Electromagnetic Scattering with Automatic Differentiation

TL;DR

torchGDM is a GPU-accelerated Python toolkit for multi-scale nano-optical simulations using Green's Dyadic Method, enabling automatic differentiation for optimization and machine learning applications in nano-photonics.

Contribution

It introduces a flexible, GPU-enabled framework in PyTorch that combines discretized and effective dipole models for complex nano-optical simulations with automatic differentiation.

Findings

Supports 3D and 2D infinite structures

Enables efficient derivative calculations for optimization

Facilitates integration with machine learning workflows

Abstract

We present "torchGDM", a numerical framework for nano-optical simulations based on the Green's Dyadic Method (GDM). This toolkit combines a hybrid approach, allowing for both fully discretized nano-structures and structures approximated by sets of effective electric and magnetic dipoles. It supports simulations in three dimensions and for infinitely long, two-dimensional structures. This capability is particularly suited for multi-scale modeling, enabling accurate near-field calculations within or around a discretized structure embedded in a complex environment of scatterers represented by effective models. Importantly, torchGDM is entirely implemented in PyTorch, a well-optimized and GPU-enabled automatic differentiation framework. This allows for the efficient calculation of exact derivatives of any simulated observable with respect to various inputs, including positions, wavelengths or permittivity, but also intermediate parameters like Green's tensor components, which can be interesting for physics informed deep learning applications. We anticipate that this toolkit will be valuable for applications merging nano-photonics and machine learning, as well as for solving nano-photonic optimization and inverse problems, such as the global design and characterization of metasurfaces, where optical interactions between structures are critical.