Editorial: Machine-learning-assisted photonic design: from fundamental physics to advanced devices

demonstrate that the method can effectively reproduce complex nonlinear dynamics, rediscover known analytical closures, and provide reduced-order descriptions even for systems lacking exact analytical formulations.Physics-inspired neural architectures also play an increasingly important role in inverse problems arising in optical sensing and spectroscopy. In medical and biomedical imaging, retrieving optical properties from diffuse scattering media remains computationally demanding when based solely on traditional diffusion theory. Witteveen et al. address this challenge by developing a physics-inspired neural network that extracts physiological parameters from diffuse reflectance spectroscopy and hyperspectral imaging data. Trained on diffusion-theory-generated datasets, the physics-driven model achieves accurate retrieval of quantities such as blood volume fraction and scattering amplitudes while dramatically reducing computational cost relative to conventional inversion methods.Machine learning techniques are likewise proving valuable in parameter optimization and experimental design problems. In mid-infrared spectroscopy, protein and peptide characterization requires identifying optimal wavelength ranges across broad spectral domains. Aledda et al. investigate several machine learning strategies for wavelength selection in tunable laser systems, including sparse modeling, filter-based approaches, and compression algorithms, and introduce a new method, w-CovSel. Their work demonstrates how data-driven optimization can improve the performance and efficiency of laser-based analytical instruments tailored to specific biochemical applications such as peptide analysis.This collection also highlights the growing shift toward photonics for machine learning, where optical hardware itself is used to accelerate computational tasks. While much of the current research in the field applies ML to photonic design, an emerging direction seeks to embed machine learning algorithms directly within optical systems to achieve ultrafast and energy-efficient information processing. Mahmoud et al. contribute to this direction by proposing an Optical Particle Swarm Optimization (OPSO) algorithm that employs an all-optical computational update mechanism. By exploiting the natural properties of coherent optical dynamics, such as nonlinear light-matter interactions and complex-domain computation, their approach demonstrates that optical computing can significantly enhance search-space exploration and improve dyslexia detection performance using eye-tracking data.Taken together, the studies presented in this Research Topic indicate that the field of photonics is entering a new era characterized by the synergistic integration of physics-informed artificial intelligence, nonlinear optical science, and optical computing hardware. The field is evolving beyond computationally assisted optimization toward increasingly autonomous and intelligent photonic design paradigms, where machine learning algorithms operate under explicit physical constraints and interact directly with optical hardware platforms.Looking forward, substantial challenges remain. Progress will require access to large, highquality datasets, while experimental data acquisition -particularly in sensitive fields such as biophotonics -often remains costly and difficult. Furthermore, transitioning from software-based machine learning models to scalable physical optical neural networks presents major challenges associated with scalability, robustness, and signal-to-noise limitations. Nevertheless, the contributions collected in this issue provide a strong foundation for addressing these obstacles and point toward a future in which machine learning and photonics become deeply integrated components of a unified technological framework for scientific discovery and advanced device engineering.