[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117524-en":3,"doc-seo-117524-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117524,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Agents Leveraging Digital Twins for Failure Prediction in Optical Networks - (2025) - IEEE ICMLCN Paper","This study proposes an advanced framework for predicting optical amplifier failures in optical networks by integrating Digital Twins (DT) and Machine Learning (ML). Using the GNPy open-source framework, Digital Twins replicate amplifier behavior under diverse conditions and generate telemetry capturing both short-term dynamics and long-term performance trends. The simulated telemetry trains an LSTM model that achieves 98% classification accuracy for amplifier fault levels, identifying soft failures and estimating severity. The controlled DT environment supports dataset creation that is hard to obtain from live networks, enabling earlier detection, reduced service disruptions, and improved operational continuity for optical communications.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine Learning Agents Leveraging Digital Twins for Failure Prediction in Optical Networks  \nOriginal  \nMachine Learning Agents Leveraging Digital Twins for Failure Prediction in Optical Networks / Mohamed, Mashboob Cheruvakkadu; Masood, Muhammad Umar; Ambrosone, Renato; Malik, Gulmina; D'Ingillo, Rocco; Straullu, Stefano; Bhyri, Sai Kishore; Galimberti, Gabriele Maria; Pedro, João; Napoli, Antonio; Wakim, Walid; Curri, Vittorio. - (2025), pp. 1-6. (Intervento presentato al convegno 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN) tenutosi a Barcelona (Spa) nel 26-29 May 2025) [10 . 1109/icmlcn64995 .2025. 11140450] .  \nAvailability:  \nThis version is available at: 11583/3002800 since: 2025-09-04T14:16:52Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/icmlcn64995.2025.11140450  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n04 October 2025  \nMachine Learning Agents Leveraging Digital Twins for Failure Prediction in Optical Networks  \nMashboob Cheruvakkadu Mohamed Politecnico di Torino, Italy  \nmashboob.cheruvakkadu@polito.it  \nMuhammad Umar Masood Renato Ambrosone Gulmina Malik  \nPolitecnico di Torino, Italy Politecnico di Torino, Italy Politecnico di Torino, Italy muhammad.masood@polito.it renato.ambrosone@polito.it gulmina.malik@polito.it  \nRocco D’Ingillo  \nPolitecnico di Torino, Italy rocco.dingillo@polito.it  \nStefano Straullu  \nLinks Foundation, Torino, Italy [stefano.straullu@linksfoundation.com](stefano.straullu@linksfoundation.com)  \nSai Kishore Bhyri  \nIn􀀂nera India sbhyri@in􀀂nera.com  \nGabriele Maria Galimberti  \nIn􀀂nera USA ggalimberti@in􀀂nera.com  \nJo˜ao Pedro  \nIn􀀂nera Unipessoal Lda, Portugal jpedro@in􀀂nera.com  \nAntonio Napoli  \nIn􀀂nera Germany anapoli@in􀀂nera.com  \nWalid Wakim  \nIn􀀂nera USA wwakim@in􀀂nera.com  \nVittorio Curri  \nPolitecnico di Torino, Italy vittorio.curri@polito.it  \nAbstract—This study proposes an advanced framework for predicting optical ampli􀀂er failures in optical networks by integrating Digital Twins (DT) and Machine Learning (ML). Utilizing the GNPy open-source framework, DTs replicate ampli􀀂er behavior under various conditions, resembling faults and captures the network conditions and performance metrics of the optical networks. The telemetry data generated from these simulations represents both short-term dynamics and long-term trends in ampli􀀂er performance, enabling the training of a Long Short-Term Memory (LSTM) model. The ML model demonstrates an ‘ampli􀀂er fault level’ classi􀀂cation accuracy of 98 %, effectively identifying soft failures and assessing fault severity. By leveraging the ability to model complex fault scenarios in a controlled environment, the framework provides a comprehensive solution for generating datasets that are otherwise dif􀀂cult to obtain from live networks. This approach enables early detection and intervention, minimizes service disruptions, and enhances network reliability. The integration of DTs and LSTM-based ML offers a scalable and data-driven solution for improving the resilience, ef􀀂ciency, and operational continuity of modern optical communication systems.  \nIndex Terms—Machine Learning, Digital Twin, Optical Ampli􀀂ers Fault Detection, Soft failures, Proactive Failure Management, Network Resilience.  \nI. INTRODUCTION  \nToday’s optical networks are constrained by rigid operational approaches, which can","cbCaitcPpLgyZSXV","https://ap.wps.com/l/cbCaitcPpLgyZSXV","pdf",521993,1,7,"English","en",105,"# Abstract\n# Index Terms\n# Introduction\n## Motivation for proactive maintenance\n## Failure management with ML\n## Dataset imbalance challenge\n## Digital Twins as training-data alternative","[{\"question\":\"What is the proposed framework for failure prediction in optical networks?\",\"answer\":\"The framework integrates Digital Twins (DT) and Machine Learning (ML) to predict optical amplifier failures using simulation-generated telemetry data.\"},{\"question\":\"How are training data and amplifier behavior represented?\",\"answer\":\"Digital Twins built with the GNPy framework replicate amplifier behavior under various conditions and produce telemetry capturing short-term dynamics and long-term trends.\"},{\"question\":\"What ML model is trained and how accurate is it?\",\"answer\":\"An LSTM model is trained on the telemetry data and reports 98% classification accuracy for amplifier fault level, enabling detection of soft failures and fault severity assessment.\"}]","Machine Learning Agents Leveraging Digital Twins for Failure Prediction in Optical Networks - 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