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Data-driven acceleration of photonic simulations.

Rahul Trivedi^1,2, Logan Su³, Jesse Lu⁴

¹E. L. Ginzton Laboratory, Stanford University, Stanford, CA, 94305, USA. rtrivedi@stanford.edu.

Scientific Reports

|December 25, 2019

Summary

This summary is machine-generated.

This study accelerates electromagnetic simulations for photonic devices using machine learning. Data-driven models predict solution subspaces, reducing computational time for Maxwell

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Area of Science:

Computational Electromagnetics
Photonic Device Design
Machine Learning Applications

Background:

Designing photonic devices requires extensive electromagnetic simulations.
Optimization procedures involve simulating numerous correlated devices, demanding significant computational resources.
Accelerating these simulations is crucial for efficient device development.

Purpose of the Study:

To investigate accelerating electromagnetic simulations using data from correlated simulations.
To develop a machine learning-based approach for enhancing simulation efficiency.
To reduce the computational cost of solving frequency-domain Maxwell's equations.

Main Methods:

Utilized principal component analysis (PCA) and a convolutional neural network (CNN).
Trained machine learning models to predict solution subspaces for frequency-domain Maxwell's equations.
Augmented the Krylov subspace in the Generalized Minimal Residual (GMRES) algorithm with predicted subspaces.

Main Results:

Achieved an order of magnitude reduction in GMRES iterations (approximately 10-50x).
Demonstrated the effectiveness of data-driven models in accelerating simulations for wavelength-splitting gratings.
Significantly reduced the number of iterations needed to solve Maxwell's equations.

Conclusions:

Machine learning can effectively accelerate electromagnetic simulations for photonic devices.
The proposed approach offers a substantial speedup in solving frequency-domain Maxwell's equations.
This method has the potential to streamline the design and optimization of complex photonic structures.