# Data-driven acceleration of photonic simulations

@article{Trivedi2019DatadrivenAO, title={Data-driven acceleration of photonic simulations}, author={Rahul Trivedi and Logan Su and Jesse Lu and Martina Schubert and Jelena Vu{\vc}kovi{\'c}}, journal={Scientific Reports}, year={2019}, volume={9} }

Designing modern photonic devices often involves traversing a large parameter space via an optimization procedure, gradient based or otherwise, and typically results in the designer performing electromagnetic simulations of a large number of correlated devices. In this paper, we investigate the possibility of accelerating electromagnetic simulations using the data collected from such correlated simulations. In particular, we present an approach to accelerate the Generalized Minimal Residual…

## 24 Citations

### NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation

- Computer ScienceArXiv
- 2022

For the first time, a physics-agnostic neural operator-based framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation and demonstrates 2-orders-of-magnitude faster simulation speed than numerical solvers, and outperforms prior neural networks.

### Deep neural networks for the evaluation and design of photonic devices

- Computer ScienceNature Reviews Materials
- 2020

This Review discusses how deep neural networks can serve as surrogate electromagnetic solvers, inverse modelling tools and global device optimizers, and how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers.

### Simulation acceleration for transmittance of electromagnetic waves in 2D slit arrays using deep learning

- Computer ScienceScientific Reports
- 2020

A deep learning approach is introduced that accelerates a simulator solving frequency-domain Maxwell equations and achieves high accuracy while predicting transmittance per wavelength in 2D slit arrays under certain conditions to achieve 160,000 times faster results than the simulator.

### Photonic Inverse Design with Neural Networks: The Case of Invisibility in the Visible

- Computer Science
- 2020

A fully connected neural network is proposed to address the problem of invisible nanoparticles by learning the dynamics of visible-light interaction with low-scattering multilayered nanospheres, and can be generalized to approximate Maxwell interactions by simulating the electromagnetic response of more complicated optical configurations.

### Deep learning for topological photonics

- PhysicsAdvances in Physics: X
- 2022

ABSTRACT In this paper, we review the specific field that combines topological photonics and deep learning (DL). Recent progress of topological photonics has attracted enormous interest for its novel…

### MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations

- Computer ScienceAPL Photonics
- 2022

This paper presents a novel scheme to train a DNN that solves Maxwell's equations speedily and accurately without relying on other computational electromagnetic solvers, and exploits the speed of this network in a novel inverse design scheme to design a micro-lens that maximizes the desired merit function.

### WaveY-Net: physics-augmented deep-learning for high-speed electromagnetic simulation and optimization

- PhysicsOPTO
- 2022

We introduce WaveY-Net, a hybrid data- and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultra fast speeds and high accuracy for entire…

### Deep learning for the design of photonic structures

- Computer Science
- 2020

Recent progress in deep-learning-based photonic design is reviewed by providing the historical background, algorithm fundamentals and key applications, with the emphasis on various model architectures for specific photonic tasks.

### Deep learning in nano-photonics: inverse design and beyond

- PhysicsPhotonics Research
- 2021

A critical review on the capabilities of deep learning for inverse design and the progress which has been made so far, and classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications.

### Interfacing photonics with artificial intelligence: an innovative design strategy for photonic structures and devices based on artificial neural networks

- Computer SciencePhotonics Research
- 2021

This review will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices, particularly those in recent experimental works and comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field.

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