Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments

• URL: http://arxiv.org/abs/2608.29907v1
• Date: Sun, 30 Aug 2026 17:04:45 GMT
• Title: Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
• Abstract summary: Future wireless systems are expected to transform the surrounding space from a passive propagation medium to a smart electromagnetic environment.<n>A key challenge is the inverse design of metasurfaces for tailored electromagnetic propagation.<n>Here, we introduce a conditional diffusion framework for inverse design of metasurfaces from target angular scattering patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, where engineered surfaces control wave propagation, support wireless sensing, and create programmable electromagnetic fingerprints. A key challenge in realizing this vision is the inverse design of metasurfaces for tailored electromagnetic propagation. While forward analysis evaluates the response of a known geometry, the inverse task starts from a prescribed scattering signature and seeks a physically realizable structure that produces it. This inverse task is inherently nonlinear and often high-dimensional, while candidate solutions may be non-unique and provide no direct indication of practical realizability. Here, we introduce a conditional diffusion framework for inverse design of dielectric resonator metasurfaces from target angular scattering patterns. Trained on T-matrix simulated geometry-response pairs, the model learns a conditional distribution of geometries instead of a deterministic mapping, enabling multiple candidate designs for the ill-posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA-ES optimization (4.1% after 10 h) while requiring only about one minute for after-training inference. The model also produces lower error distributions than deterministic neural baselines for out-of-distribution spectra, highlighting the potential of diffusion models for efficient metasurface design.

Related papers

• Learning Metamaterial Eigenmodes with Wavelet-Encoded Fourier Neural Operators [57.64362886310329]
  We show that a machine learning surrogate based on neural operators can learn and predict multiple eigenmodes of the elastic wave equation.<n>For metamaterial design, the resulting surrogate accelerates the simulation stage of the design cycle by three orders of magnitude relative to finite element analysis on a consumer-grade CPU.<n>These results also carry broader implications for designing input encodings in other multi-mode PDE solvers based on spectral neural operators.
  arXiv  Detail & Related papers  (2026-09-08T01:21:46Z)
• EFF-Grasp: Energy-Field Flow Matching for Physics-Aware Dexterous Grasp Generation [54.09734511705173]
  EFF-Grasp is a Flow-Matching synthesis-based framework for physics-aware dexterous grasp generation.<n>We reformulate grasp as a deterministic ordinary differential equation (ODE) process, which enables efficient and stable generation through smooth probability flows.<n> EFF-Grasp achieves superior performance in grasp quality and physical feasibility, while requiring substantially fewer sampling steps than diffusion-based baselines.
  arXiv  Detail & Related papers  (2026-03-17T06:11:56Z)
• Geometry-Free Conditional Diffusion Modeling for Solving the Inverse Electrocardiography Problem [0.5729426778193398]
  We present a conditional diffusion framework that learns a probabilistic mapping from noisy body surface signals to heart surface electric potentials.<n>The proposed approach leverages the generative nature of diffusion models to capture the non-unique and underdetermined nature of the ECGI inverse problem.<n>We evaluate the method on a real ECGI dataset and compare it against strong deterministic baselines, including a convolutional neural network, long short-term memory network, and transformer-based model.
  arXiv  Detail & Related papers  (2026-01-26T15:53:54Z)
• 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)
• Physics-Guided Conditional Diffusion Networks for Microwave Image Reconstruction [0.6117371161379209]
  A conditional latent-diffusion based framework for solving the electromagnetic inverse scattering problem is introduced.<n>The proposed model generates multiple plausible permittivity maps conditioned on measured scattered-field data.<n>A forward electromagnetic solver is integrated into the reconstruction pipeline as a physics-based evaluation mechanism.
  arXiv  Detail & Related papers  (2025-10-29T17:34:10Z)
• 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)
• A Surrogate Model for the Forward Design of Multi-layered Metasurface-based Radar Absorbing Structures [3.328784252410173]
  We propose a surrogate model that significantly accelerates the prediction of electromagnetic (EM) responses of multi-layered metasurface-based RAS.<n>The proposed model achieved a cosine similarity of 99.9% and a mean square error of 0.001 within 1000 epochs of training.
  arXiv  Detail & Related papers  (2025-05-14T09:54:00Z)
• RadioDiff-$k^2$: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction [76.24833675757033]
  We propose a physics-informed generative learning approach, named RadioDiff-$k2$, for accurate and efficient multipath-aware radio map (RM) construction.<n>We show that the proposed RadioDiff-$k2$ framework achieves state-of-the-art (SOTA) performance in both image-level RM construction and localization tasks.
  arXiv  Detail & Related papers  (2025-04-22T06:28:13Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• VTAE: Variational Transformer Autoencoder with Manifolds Learning [144.0546653941249]
  Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables. The nonlinearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. We show that geodesics and accurate computation can substantially improve the performance of deep generative models.
  arXiv  Detail & Related papers  (2023-04-03T13:13:19Z)
• An Energy-Based Prior for Generative Saliency [62.79775297611203]
  We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps consistent with human perception.
  arXiv  Detail & Related papers  (2022-04-19T10:51:00Z)
• Targeted free energy estimation via learned mappings [66.20146549150475]
  Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences. FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions. One strategy to mitigate this problem, called Targeted Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase overlap.
  arXiv  Detail & Related papers  (2020-02-12T11:10:00Z)

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.