4 citations · 9 across the 7 of their papers we have counts for
8 papers
Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management
Yao Zhang, Shengnan Li, Yuchen Song +11
As optical networks continue to expand in scale, complexity, and service diversity, the implementation of automation has become essential for ensuring agility, efficiency, and reli…
Mathematical Reasoning Enhanced LLM for Formula Derivation: A Case Study on Fiber NLI Modellin
Yao Zhang, Yuchen Song, Xiao Luo +4
Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for symbolic physical reasoning in…
Large language models for optical network O&M: Agent-embedded workflow for automation
Shengnan Li, Yidi Wang, Fubin Wang +7
With the continuous expansion of optical networks and the increasing diversity of services, existing operation and maintenance (O&M) approaches are increasingly challenged to meet…
FiberKAN: Kolmogorov-Arnold Networks for Nonlinear Fiber Optics
Xiaotian Jiang, Min Zhang, Xiao Luo +3
Scientific discovery and dynamic characterization of the physical system play a critical role in understanding, learning, and modeling the physical phenomena and behaviors in vario…
Physics-Informed Machine Learning for EDFA: Parameter Identification and Gain Estimation
Xiaotian Jiang, Jiawei Dong, Yuchen Song +3
As the key component that facilitates long-haul transmission in optical fiber communications by increasing capacity and reducing costs, accurate characterization and gain settings…
When Large Language Models Meet Optical Networks: Paving the Way for Automation
Danshi Wang, Yidi Wang, Xiaotian Jiang +3
Since the advent of GPT, large language models (LLMs) have brought about revolutionary advancements in all walks of life. As a superior natural language processing (NLP) technology…
Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission
Yuchen Song, Danshi Wang, Qirui Fan +3
We propose efficient modelling of optical fiber channel via NLSE-constrained physics-informed neural operator without reference solutions. This method can be easily scalable for di…
Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics
Xiaotian Jiang, Danshi Wang, Qirui Fan +3
A physics-informed neural network (PINN) that combines deep learning with physics is studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber op…