A deep learning approach for inverse design of the metasurface for dual-polarized waves

• URL: http://arxiv.org/abs/2105.08508v1
• Date: Wed, 12 May 2021 17:15:31 GMT
• Title: A deep learning approach for inverse design of the metasurface for dual-polarized waves
• Authors: Fardin Ghorbani, Javad Shabanpour, Sina Beyraghi, Hossein Soleimani, Homayoon Oraizi, Mohammad Soleimani
• Abstract summary: Machine learning-based methods have recently created an inspiring platform for an inverse realization of the metasurfaces. Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM waves.
• Score: 1.1254693939127909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compared to the conventional metasurface design, machine learning-based methods have recently created an inspiring platform for an inverse realization of the metasurfaces. Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM polarized waves. To automatically generate metasurfaces in a wide range of working frequencies from 4 to 45 GHz, we deliberately design an 8 ring-shaped pattern in such a way that the unit-cells generated in the dataset can produce single or multiple notches in the desired working frequency band. Compared to the general approach, whereby the final metasurface structure may be formed by any randomly distributed "0" and "1", we propose here a restricted output structure. By restricting the output, the number of calculations will be reduced and the learning speed will be increased. Moreover, we have shown that the accuracy of the network reaches 91\%. Obtaining the final unit cell directly without any time-consuming optimization algorithms for both TE and TM polarized waves, and high average accuracy, promises an effective strategy for the metasurface design; thus, the designer is required only to focus on the design goal.

Related papers

• Diffusion-Based Electromagnetic Inverse Design of Scattering Structured Media [0.0]
  conditional diffusion model for electromagnetic inverse design generates structured media geometries directly from target differential scattering cross-section profiles.<n>Trained on 11,000 simulated metasurfaces, the model achieves median MPE below 19% on unseen targets.
  arXiv  Detail & Related papers  (2025-11-07T15:48:50Z)
• Chat to Chip: Large Language Model Based Design of Arbitrarily Shaped Metasurfaces [1.7706010980924418]
  We show that an LLM can learn the physical relationships needed for spectral prediction and inverse design.<n>This "chat-to-chip" workflow represents a step toward more user-friendly data-driven nanophotonics.
  arXiv  Detail & Related papers  (2025-09-29T02:24:57Z)
• Wavelet Logic Machines: Learning and Reasoning in the Spectral Domain Without Neural Networks [0.0]
  We introduce a fully spectral learning framework that eliminates traditional neural layers by operating entirely in the wavelet domain.<n>The model applies learnable nonlinear transformations, including soft-thresholding and gain-phase modulation, directly to wavelet coefficients.<n>It also includes a differentiable wavelet basis selection mechanism, enabling adaptive processing using families such as Haar, Daubechies, and Biorthogonal wavelets.
  arXiv  Detail & Related papers  (2025-07-18T01:28:17Z)
• Inverse Design of Diffractive Metasurfaces Using Diffusion Models [28.865660196923752]
  Metasurfaces are ultra-thin optical elements composed of engineered sub-wavelength structures that enable precise control of light.<n>In inverse design, determining a geometry that yields a desired optical response is challenging due to the complex, nonlinear relationship between structure and optical properties.<n>We address these challenges by integrating the generative capabilities of diffusion models into computational design.<n>We demonstrate our approach on the design of a spatially uniform intensity splitter and a polarization beam splitter, both produced with low error in under 30 minutes.
  arXiv  Detail & Related papers  (2025-06-26T20:10:30Z)
• Onboard Terrain Classification via Stacked Intelligent Metasurface-Diffractive Deep Neural Networks from SAR Level-0 Raw Data [18.05218019915147]
  This paper introduces a novel approach for real-time onboard terrain classification from Sentinel-1 (S1) level-0 raw In-phase/Quadrature (IQ) data. Our method helps bridge the gap between next-generation remote sensing tasks and in-orbit processing needs, paving the way for computationally efficient remote sensing applications.
  arXiv  Detail & Related papers  (2025-03-10T09:25:44Z)
• Dynamic Frame Interpolation in Wavelet Domain [57.25341639095404]
  Video frame is an important low-level computation vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts.
  arXiv  Detail & Related papers  (2023-09-07T06:41:15Z)
• Artificial Intelligence-Generated Terahertz Multi-Resonant Metasurfaces via Improved Transformer and CGAN Neural Networks [2.1592777170316366]
  We propose improved Transformer and conditional generative adversarial neural networks (CGAN) for the inverse design of graphene metasurfaces. The improved Transformer can obtain higher accuracy and generalization performance in the StoV (Spectrum to Vector) spectra. CGAN can achieve the inverse design of graphene metasurface images directly from the desired multi-resonant absorption spectra.
  arXiv  Detail & Related papers  (2023-07-21T02:49:03Z)
• One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers [57.441926088870325]
  Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements. Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
  arXiv  Detail & Related papers  (2023-06-06T20:28:37Z)
• Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms [0.6999740786886535]
  We present a novel approach for the design of leaky-wave holographic antennas that generates OAM-carrying electromagnetic waves by combining Flat Optics (FO) and machine learning (ML) techniques. To improve the performance of our system, we use a machine learning technique to discover a mathematical function that can effectively control the entire radiation pattern. We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77,000 generated datasets.
  arXiv  Detail & Related papers  (2023-04-25T10:01:04Z)
• Transform Once: Efficient Operator Learning in Frequency Domain [69.74509540521397]
  We study deep neural networks designed to harness the structure in frequency domain for efficient learning of long-range correlations in space or time. This work introduces a blueprint for frequency domain learning through a single transform: transform once (T1)
  arXiv  Detail & Related papers  (2022-11-26T01:56:05Z)
• Deep-Learning Empowered Inverse Design for Freeform Reconfigurable Metasurfaces [3.728073286482581]
  We present a deep-learning empowered inverse design method for freeform reconfigurable metasurfaces. A convolutional neural network model is trained to predict the responses of free-shaped meta-atoms. An inverse-designed wideband reconfigurable metasurface prototype is fabricated and measured for beam scanning applications.
  arXiv  Detail & Related papers  (2022-11-11T15:01:32Z)
• Robustness of a universal gate set implementation in transmon systems via Chopped Random Basis optimal control [50.591267188664666]
  We numerically study the implementation of a universal two-qubit gate set, composed of CNOT, Hadamard, phase and $pi/8$ gates, for transmon-based systems. The control signals to implement such gates are obtained using the Chopped Random Basis optimal control technique, with a target gate infidelity of $10-2$.
  arXiv  Detail & Related papers  (2022-07-27T10:55:15Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Designing globally optimal entangling gates using geometric space curves [0.0]
  We show that in the case of weakly coupled qubits, it is possible to find all pulses that implement a target entangling gate. We illustrate our method by designing fast, CNOT-equivalent entangling gates for silicon quantum dot spin qubits with fidelities exceeding 99%.
  arXiv  Detail & Related papers  (2022-04-06T14:46:56Z)
• Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation [10.775558382613077]
  Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a deep learning-based approach that directly optimize the beamformers at the base station according to the received uplink pilots. A neural calibration method is proposed to improve the scalability of the end-to-end design.
  arXiv  Detail & Related papers  (2021-08-03T14:26:14Z)
• Progressive Encoding for Neural Optimization [92.55503085245304]
  We show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations.
  arXiv  Detail & Related papers  (2021-04-19T08:22:55Z)

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.