Beyond white- and black-box modeling tools in optical communications and optical computing: physics-informed data-driven modeling

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.