AI Transforming Photonics Design: Unlocking Next-Generation Efficiency

The fusion of artificial intelligence (AI) with physics-guided frameworks has revolutionised electromagnetic and nanophotonic systems design. Innovations in deep neural networks (DNNs) and physics-informed neural networks (PINNs) address challenges in light scattering, meta-photonics, and nonlinear photonics. AI-driven forward and inverse design strategies replace trial-and-error approaches by embedding physical laws into optimisation workflows. These methods accelerate electromagnetic simulations, enabling precise modelling of complex effects like topological photonic states and nonlinear interactions. While challenges such as model interpretability and data scarcity persist, future opportunities lie in scalable multi-physics modelling and practical deployment of AI-optimised devices.

Recent advancements in artificial intelligence (AI) integrated with physics-guided frameworks have significantly impacted electromagnetic and nanophotonic system design, addressing challenges in light scattering, meta-, and nonlinear photonics. A collaborative effort led by Omar A. M. Abdelraouf from A*STAR’s Institute of Materials Research and Engineering in Singapore, along with Abdulrahman M. A. Ahmed, Emadeldeen Eldele, and Ahmed A. Omar et al., explores these innovations in their review titled ‘Physics-Informed Neural Networks in Electromagnetic and Nanophotonic Design.’ The article examines AI-driven strategies that enhance device performance through efficient optimization by embedding physical laws into workflows, accelerating electromagnetic simulations, and modeling complex phenomena like topological photonic states and nonlinear interactions. The review evaluates algorithmic frameworks’ strengths in efficiency and multi-objective optimization while addressing challenges such as model interpretability and data scarcity. Looking ahead, the authors identify opportunities in scalable multi-physics modelling and practical deployment of AI-optimized devices.

Machine learning accelerates design processes across engineering and materials science.

Materials science has seen a growing interest in inverse design approaches, which aim to determine the structural or compositional properties of materials based on desired functional outcomes. Traditional methods for designing materials often rely on trial-and-error processes, which can be time-consuming and resource-intensive. To address these challenges, researchers have increasingly turned to machine learning techniques, particularly deep generative models, to accelerate the discovery and design of novel materials.

In the context of metamaterials—artificially engineered structures with properties not found in nature—Ma et al. (2019) introduced a probabilistic framework for inverse design using deep generative models combined with semi-supervised learning strategies. This approach enables efficient exploration of material properties by mapping desired functionalities to feasible designs, significantly reducing the time and effort required for material discovery.

Similarly, in the field of antenna array optimisation, Wei et al. (2022) developed a fully automated design method that integrates reinforcement learning with surrogate modelling. Their work addresses mutual coupling effects in antenna arrays, which can degrade performance by causing interference between adjacent antennas. By automating the design process, their approach not only improves performance but also reduces the complexity of manual optimization.

Both studies highlight the potential of machine learning techniques to revolutionize design processes across different engineering domains. While Ma et al.’s work focuses on materials science and metamaterials, Wei et al.’s research addresses challenges in antenna array optimization, demonstrating the versatility of these approaches in tackling complex design problems. Together, they underscore the growing role of artificial intelligence in accelerating innovation and improving efficiency in various fields of engineering and applied sciences.

Computational techniques solve complex problems by integrating physical laws.

Researchers have developed innovative methods across various fields, each addressing specific challenges with novel approaches. In radar applications, support vector machines (SVMs) are employed to process data from antenna arrays, enhancing signal detection and discrimination. SVMs, known for their effectiveness in classification tasks, offer a robust solution for improving radar performance.

Engineers have designed a wideband circularly polarized antenna using non-uniform metasurfaces optimized through multi-objective Bayesian methods. This approach efficiently balances conflicting objectives, resulting in an antenna with enhanced performance characteristics, suitable for diverse applications requiring precise control over electromagnetic properties.

In optics, scientists utilize random metasurfaces combined with Bayesian optimization to create efficient optical filters. By leveraging the inherent variations of these surfaces and optimising them computationally, they achieve desired filter traits without needing meticulous structural control, offering a flexible and scalable solution.

Lastly, researchers have developed physics-informed neural networks (PINNs) to model electromagnetic problems in discontinuous media. These networks integrate physical laws into their training, enabling accurate modelling of complex electromagnetic behaviours where traditional methods may fall short. This approach is particularly valuable for materials with abrupt property changes, providing a powerful tool for simulations and predictions.

Each method represents a significant advancement in its field, offering improved performance, efficiency, and applicability to complex scenarios. These innovations enhance current technologies and pave the way for future developments across radar, antenna design, optics, and electromagnetic modeling.

ML enhances electromagnetic design via diverse tools.

Integrating machine learning (ML) into electromagnetic structure design has demonstrated significant potential for addressing complex optimisation challenges in antenna and metasurface engineering. Techniques such as deep learning, reinforcement learning, Bayesian optimisation, and support vector machines have been successfully applied to tasks ranging from inverse design to multi-objective optimisation, focusing on enhancing performance metrics like bandwidth, efficiency, and radiation patterns. Physics-informed approaches, which combine ML with physical laws, have shown promise in improving model accuracy and computational efficiency.

However, several challenges remain. The computational demands of deep learning models require careful trade-offs and optimisations during training. Simulation complexity is often managed through approximations or reduced-order models, but this raises questions about the generalisability of ML models to different structures. Ensuring model reliability and validation remains critical, as does addressing concerns about whether these approaches can scale effectively beyond specific applications.

Future work should focus on improving computational efficiency while maintaining accuracy, exploring methods to enhance model generalisability, and conducting rigorous comparisons with traditional design techniques. Industry adoption will likely depend on demonstrating the practical benefits of ML through real-world case studies that highlight its transformative potential compared to conventional approaches.