PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation

TL;DR

This paper introduces PIDT, a physics-informed digital twin that enhances optical fiber parameter estimation by integrating a physics-based split-step method with a specialized loss function, achieving better accuracy and efficiency.

Contribution

The paper presents a novel PIDT framework that combines physics-informed modeling with neural networks for improved fiber parameter estimation.

Findings

PIDT outperforms previous neural operators in accuracy.

PIDT converges faster than traditional methods.

Lower complexity in computation compared to existing approaches.

Abstract

We propose physics-informed digital twin (PIDT): a fiber parameter estimation approach that combines a parameterized split-step method with a physics-informed loss. PIDT improves accuracy and convergence speed with lower complexity compared to previous neural operators.