Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning

• URL: http://arxiv.org/abs/2204.00433v1
• Date: Mon, 28 Mar 2022 02:21:03 GMT
• Title: Inverse Design and Experimental Verification of a Bianisotropic Metasurface Using Optimization and Machine Learning
• Authors: Stewart Pearson, Parinaz Naseri, and Sean V. Hum
• Abstract summary: Many metasurface designs start with a set of constraints for the radiated far-field, and end with a non-uniform physical structure for the surface. Here, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electromagnetic metasurfaces have attracted significant interest recently due to their low profile and advantageous applications. Practically, many metasurface designs start with a set of constraints for the radiated far-field, such as main-beam direction(s) and side lobe levels, and end with a non-uniform physical structure for the surface. This problem is quite challenging, since the required tangential field transformations are not completely known when only constraints are placed on the scattered fields. Hence, the required surface properties cannot be solved for analytically. Moreover, the translation of the desired surface properties to the physical unit cells can be time-consuming and difficult, as it is often a one-to-many mapping in a large solution space. Here, we divide the inverse design process into two steps: a macroscopic and microscopic design step. In the former, we use an iterative optimization process to find the surface properties that radiate a far-field pattern that complies with specified constraints. This iterative process exploits non-radiating currents to ensure a passive and lossless design. In the microscopic step, these optimized surface properties are realized with physical unit cells using machine learning surrogate models. The effectiveness of this end-to-end synthesis process is demonstrated through measurement results of a beam-splitting prototype.

Related papers

• Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
  We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization. Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information. Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
  arXiv  Detail & Related papers  (2024-05-23T14:39:52Z)
• Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces [8.223940676615857]
  Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. One fundamental challenge of these systems is their ability to represent nonlinear relationships between sets of data that have different dimensionalities. Existing design methods often implement a conditional Generative Adversarial Network in order to solve this problem, but in many cases the solution is unable to generate structures that provide multiple peaks when validated. It is demonstrated that in response to the target spectrum, the Bidirectionalluminescent Adversarial Autoencoder is able to generate structures that provide multiple peaks on several occasions.
  arXiv  Detail & Related papers  (2024-05-07T06:57:42Z)
• Molecule Design by Latent Prompt Transformer [76.2112075557233]
  This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task. We propose a novel generative model comprising three components: (1) a latent vector with a learnable prior distribution; (2) a molecule generation model based on a causal Transformer, which uses the latent vector as a prompt; and (3) a property prediction model that predicts a molecule's target properties and/or constraint values using the latent prompt.
  arXiv  Detail & Related papers  (2024-02-27T03:33:23Z)
• Tailoring Frictional Properties of Surfaces Using Diffusion Models [0.0]
  This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model. We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes.
  arXiv  Detail & Related papers  (2024-01-05T09:15:07Z)
• A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design [11.692790232334293]
  We present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology.
  arXiv  Detail & Related papers  (2023-10-18T09:59:35Z)
• Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
  This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
  arXiv  Detail & Related papers  (2023-08-10T06:40:19Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Minimal Neural Atlas: Parameterizing Complex Surfaces with Minimal Charts and Distortion [71.52576837870166]
  We present Minimal Neural Atlas, a novel atlas-based explicit neural surface representation. At its core is a fully learnable parametric domain, given by an implicit probabilistic occupancy field defined on an open square of the parametric space. Our reconstructions are more accurate in terms of the overall geometry, due to the separation of concerns on topology and geometry.
  arXiv  Detail & Related papers  (2022-07-29T16:55:06Z)
• Topology-Adaptive Mesh Deformation for Surface Evolution, Morphing, and Multi-View Reconstruction [35.01330182954581]
  We introduce a new self-intersection removal algorithm, TransforMesh, and we propose a mesh evolution framework based on this algorithm. We describe two challenging applications, namely surface morphing and 3-D reconstruction.
  arXiv  Detail & Related papers  (2020-12-10T09:26:40Z)
• AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection [76.7063732501752]
  We provide a new identity swapping algorithm with large differences in appearance for face forgery detection. The appearance gaps mainly arise from the large discrepancies in illuminations and skin colors. A discriminator is introduced to distinguish the fake parts from a mix of real and fake image patches.
  arXiv  Detail & Related papers  (2020-11-05T06:17:04Z)

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.