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A deep neural network for general scattering matrix.

Yongxin Jing¹, Hongchen Chu¹, Bo Huang²

¹National Laboratory of Solid State Microstructures, School of Physics, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

Nanophotonics (Berlin, Germany)

|December 5, 2024

Summary

This summary is machine-generated.

A novel deep neural network (DNN) efficiently computes the scattering matrix for complex objects, overcoming limitations of traditional numerical methods. This AI approach accelerates calculations by thousands of times while preserving physical laws.

Keywords:

deep neural network inverse problem scattering matrix

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Area of Science:

Computational physics
Electromagnetics
Artificial intelligence

Background:

The scattering matrix characterizes how objects interact with waves.
Analytical solutions are limited to highly symmetric scatterers.
Numerical methods like finite element solvers are computationally intensive.

Purpose of the Study:

To develop a fast and accurate method for calculating scattering matrices.
To enable the analysis of scatterers lacking symmetry.
To explore inverse design capabilities for scattering phenomena.

Main Methods:

Training a deep neural network (DNN) to compute scattering matrices.
Utilizing gradient descent for inverse design applications.
Validating the DNN's ability to preserve physical principles (energy conservation, reciprocity).

Main Results:

The DNN calculates scattering matrices thousands of times faster than finite element solvers.
The computed scattering matrices inherently satisfy fundamental physical laws.
Successful inverse design of scatterers with specific scattering properties was demonstrated.

Conclusions:

Deep learning offers a computationally efficient solution for scattering problems.
The developed DNN provides a powerful tool for analyzing and designing complex scatterers.
This approach accelerates research in electromagnetics and wave scattering.