{"ID":2894005,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12299","arxiv_id":"2507.12299","title":"High-Precision Modal Analysis of Multimode Waveguides from Amplitudes via Large-Step Nonconvex Optimization","abstract":"Optimizing multimodal waveguide performance depends on modal analysis; however, existing methods focus predominantly on modal power distribution (MPD) and, limited by experimental hardware and conditions, exhibit low accuracy, poor adaptability, and high computational cost. This work presents a novel framework for comprehensive modal analysis (recovering both power and relative phase) using aperture field (AF) and far field (FF) amplitude measurements. We formulate the modal analysis as a nonconvex optimization problem under a power-normalization constraint and, inspired by recent advances in deep learning, introduce a large-step strategy to solve it. Our method retrieves both the MPD and the modal relative-phase distribution(MRPD). The effectiveness of the proposed method is validated through visualization of the nonconvex optimization process via its loss landscape. Under noiseless conditions, analysis results of $93$ electromagnetic modes indicate that the relative amplitude accuracy $\\mathrm{MRE_{Modulus}}$, and the phase accuracy $\\mathrm{MAE_{Phase}}$, both reach the level of machine precision. Through noise simulations of the AF and environmental background, the operational principles of the method are demonstrated under signal-to-noise ratio (SNR) conditions ranging from $10~\\mathrm{dB}$ to $60~\\mathrm{dB}$. Experiments further confirm that error suppression is effectively achieved by increasing the number of sampling points, thereby maintaining high accuracy and strong robustness. Within a unified evaluation framework, the absolute amplitude error $\\mathrm{MAE_{Modulus}}$, and the phase error $\\mathrm{MAE_{Phase}}$, are as low as $1.633\\times10^{-8}$ and $0$, respectively. The accuracy is significantly superior to existing methods, while also exhibiting higher computational efficiency.","short_abstract":"Optimizing multimodal waveguide performance depends on modal analysis; however, existing methods focus predominantly on modal power distribution (MPD) and, limited by experimental hardware and conditions, exhibit low accuracy, poor adaptability, and high computational cost. This work presents a novel framework for comp...","url_abs":"https://arxiv.org/abs/2507.12299","url_pdf":"https://arxiv.org/pdf/2507.12299v2","authors":"[\"Jingtong Li\",\"Dongting Huang\",\"Minhui Xiong\",\"Mingzhi Li\"]","published":"2025-07-16T14:56:03Z","proceeding":"physics.comp-ph","tasks":"[\"physics.comp-ph\",\"cs.SD\",\"physics.optics\"]","methods":"[]","has_code":false}