{"ID":6138206,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T10:54:07.539889654Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07274","arxiv_id":"2607.07274","title":"Beyond white- and black-box modeling tools in optical communications and optical computing: physics-informed data-driven modeling","abstract":"Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training data efficiency and limited generalizability. To address this trade-off, physics-informed data-driven modeling has emerged as a powerful hybrid paradigm. This paper presents a comparative analysis of these three modeling paradigms across three benchmark use cases: optical amplifiers, directly modulated lasers, and interferometer meshes. By evaluating model complexity, data efficiency, generalizability, and modularity, this work provides a detailed analysis of the respective trade-offs and highlights the advantages of combining physical insight with data-driven learning.","short_abstract":"Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training data efficiency and limited generalizabi...","url_abs":"https://arxiv.org/abs/2607.07274","url_pdf":"https://arxiv.org/pdf/2607.07274v1","authors":"[\"Isidora Teofilovic\",\"Sergio Hernandez Fernandez\",\"Metodi P. Yankov\",\"Christophe Peucheret\",\"Darko Zibar\",\"Francesco Da Ros\"]","published":"2026-07-08T11:04:54Z","proceeding":"physics.optics","tasks":"[\"physics.optics\",\"eess.SP\"]","methods":"[]","has_code":false}