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Hiroyuki Ishihara

Publications and source records attributed to Hiroyuki Ishihara.

4 recordsLinked to original sources
arXiv2026

Optical Network Digital Twin: From Digital Coherent Transmission to Intelligent Network Operations

Optical Network Digital Twins (ONDTs) are emerging as a key framework for representing, synchronizing, and utilizing the physical state of optical networks for quality-of-transmission (QoT) estimation, optimization, and intelligent operation. This survey provides a comprehensive view of ONDTs, spanning digital coherent transmission and physical-layer observability to intelligent network operations. We systematically organize ONDT technologies from ecosystem, functional, control, and implementation architecture perspectives and review the enabling technologies required to construct, synchronize, and utilize digital representations of physical optical networks. As a unifying framework to describe both ONDT design and validation, we introduce a categorization based on the following figures of merit: Fidelity, Dynamicity, and Deployability (FDD). In particular, we emphasize fit-for-purpose Fidelity, where modeling effort focuses on the physical effects of components rather than solely maximizing model accuracy, favoring scalability and generalizability. Furthermore, experimental and field validations are examined to evaluate the practical applicability of ONDT technologies. We further examine experimental and field validations, from physics-based QoT estimation and measurement-informed model refinement to field synchronization, service provisioning, multi-domain observability, and infrastructure characterization. Finally, we discuss the complementary roles of ONDTs and AI agents toward autonomous optical networks and identify key research directions for practical deployment.

eess.SY↗
arXiv2026

Leveraging Digital Twin Technologies: All-Photonics Networks-as-a-Service for Data Center Xchange in the Era of AI [Invited Tutorial]

This paper presents a data center exchange (Data Center Xchange, DCX) architecture for all-photonics networks-as-a-service in distributed data center infrastructures, enabling the creation of a virtual large-scale data center by directly interconnecting distributed data centers in metropolitan areas. Key requirements for such an architecture are identified: support for low-latency operations, scalability, reliability, and flexibility within a single network architecture; the ability to add new operator-driven automation functionalities based on an open networking approach; and the ability to control and manage remotely deployed transponders connected via access links with unknown physical parameters. We propose a set of technologies that enable digital twin operations for optical networks, including a cloud-native architecture for coherent transceivers, remote transponder control, fast end-to-end optical path provisioning, transceiver-based physical-parameter estimation incorporating digital longitudinal monitoring, and optical line system calibration, demonstrating their feasibility through field validations.

eess.SY↗
arXiv2021

A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

Machine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically go through the tedious process of inspecting the failure cases manually, identifying failure modes and then attempting to fix the model. In this work, we aim to standardise and bring principles to this process through answering two critical questions: (i) how do we know that we have identified meaningful and distinct failure types?; (ii) how can we validate that a model has, indeed, been repaired? We suggest that the quality of the identified failure types can be validated through measuring the intra- and inter-type generalisation after fine-tuning and introduce metrics to compare different subtyping methods. Furthermore, we argue that a model can be considered repaired if it achieves high accuracy on the failure types while retaining performance on the previously correct data. We combine these two ideas into a principled framework for evaluating the quality of both the identified failure subtypes and model repairment. We evaluate its utility on a classification and an object detection tasks. Our code is available at https://github.com/Rokken-lab6/Failure-Analysis-and-Model-Repairment

cs.LG↗