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

• URL: http://arxiv.org/abs/2203.01248v1
• Date: Wed, 2 Mar 2022 17:11:33 GMT
• Title: WaveY-Net: Physics-augmented deep learning for high-speed electromagnetic simulation and optimization
• Authors: Mingkun Chen, Robert Lupoiu, Chenkai Mao, Der-Han Huang, Jiaqi Jiang, Philippe Lalanne, and Jonathan A. Fan
• Abstract summary: We introduce WaveY-Net, a hybrid data- and physics-augmented convolutional neural network that can predict electromagnetic field distributions with ultra fast speeds. We show that the high speed simulator can be directly and effectively used in the local and global freeform optimization of metagratings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The calculation of electromagnetic field distributions within structured media is central to the optimization and validation of photonic devices. 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 classes of dielectric photonic structures. This accuracy is achieved by training the neural network to learn only the magnetic near-field distributions of a system and to use a discrete formalism of Maxwell's equations in two ways: as physical constraints in the loss function and as a means to calculate the electric fields from the magnetic fields. As a model system, we construct a surrogate simulator for periodic silicon nanostructure arrays and show that the high speed simulator can be directly and effectively used in the local and global freeform optimization of metagratings. We anticipate that physics-augmented networks will serve as a viable Maxwell simulator replacement for many classes of photonic systems, transforming the way they are designed.

Related papers

• NeuralMAG: Fast and Generalizable Micromagnetic Simulation with Deep Neural Nets [9.674100498903844]
  We introduce NeuralMAG, a deep learning approach to micromagnetic simulation. Our approach follows the LLG iterative framework but accelerates demagnetizing field computation through the employment of a U-shaped neural network (Unet) Unlike existing neural methods, NeuralMAG concentrates on the core computation rather than an end-to-end approximation for a specific task, making it inherently generalizable.
  arXiv  Detail & Related papers  (2024-10-19T05:25:08Z)
• Unified, Verifiable Neural Simulators for Electromagnetic Wave Inverse Problems [1.6795461001108096]
  We show a single, unified model capable of addressing scattering simulations with thousands of DoFs, of any wavelength, any illumination wavefront, and freeform materials. Our findings demonstrate a path to universal, verifiably accurate neural surrogates for existing scattering simulators.
  arXiv  Detail & Related papers  (2024-03-31T03:23:29Z)
• Gradual Optimization Learning for Conformational Energy Minimization [69.36925478047682]
  Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks significantly reduces the required additional data. Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules.
  arXiv  Detail & Related papers  (2023-11-05T11:48:08Z)
• Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach [0.0]
  We present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM) Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators.
  arXiv  Detail & Related papers  (2023-06-15T12:47:56Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• Physics Embedded Machine Learning for Electromagnetic Data Imaging [83.27424953663986]
  Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. This article surveys various schemes to incorporate physics in learning-based EM imaging.
  arXiv  Detail & Related papers  (2022-07-26T02:10:15Z)
• Field Level Neural Network Emulator for Cosmological N-body Simulations [7.051595217991437]
  We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. We use two convolutional neural networks trained to output the nonlinear displacements and velocities of N-body simulation particles.
  arXiv  Detail & Related papers  (2022-06-09T16:21:57Z)
• Physics informed neural networks for continuum micromechanics [68.8204255655161]
  Recently, physics informed neural networks have successfully been applied to a broad variety of problems in applied mathematics and engineering. Due to the global approximation, physics informed neural networks have difficulties in displaying localized effects and strong non-linear solutions by optimization. It is shown, that the domain decomposition approach is able to accurately resolve nonlinear stress, displacement and energy fields in heterogeneous microstructures obtained from real-world $mu$CT-scans.
  arXiv  Detail & Related papers  (2021-10-14T14:05:19Z)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)
• Deep neural networks for the evaluation and design of photonic devices [0.0]
  Review: How deep neural networks can learn from training sets and operate as high-speed surrogate electromagnetic solvers. Fundamental data sciences framed within the context of photonics will also be discussed.
  arXiv  Detail & Related papers  (2020-06-30T19:52:54Z)
• Training End-to-End Analog Neural Networks with Equilibrium Propagation [64.0476282000118]
  We introduce a principled method to train end-to-end analog neural networks by gradient descent. We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models. Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
  arXiv  Detail & Related papers  (2020-06-02T23:38:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.