[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121230-en":3,"doc-seo-121230-105":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},121230,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","From Explainable AI to Explainable Simulation - Using Machine Learning and XAI to understand System Robustness","Evaluating robustness is a central objective in simulation-based analysis, where controllable factors are tuned so noise in uncontrollable variables minimally affects variance of the desired outputs. After a simulation model is available, large data generation enables learning hidden relationships, especially those tied to robustness. Data farming combines smart experiment design, HPC, automated analysis, and interactive visualization. Machine learning can model input-output behavior but often acts as opaque black boxes; explainable AI supports interpreting these models to study system robustness.","| Feldkamp, Niclas; Straßburger, Steffen\u003Cbr>From explainable AI to explainable simulation: using machine learning and XAIto understand system robustness |  |\n| --- | --- |\n| Original published in: | ACM SIGSIM-PADS 2023. -New York, NY : The Association for Computing Machinery. - (2023), p. 96-106. |\n| Conference: | ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation (SIGSIM PADS) (Orlando, Fla. ) : 2023.06.21-23 |\n| Original published: | 2023-06-21 |\n| ISBN: | 979-8-4007-0030-9 |\n| DOI: | 10.1145/3573900.3591114 |\n| [Visited: | 2024-08-01] |\n|  | This work is licensed under a Creative Commons Attribution 4.0\u003Cbr>International license. To view a copy of this license, visit\u003Cbr>[https://creativecommons.org/l](https://creativecommons.org/l)icenses/by/4 .0/ |\n\nTU Ilmenau | Universitätsbibliothek | ilmedia, 2024 [http://www.tu-ilmenau.de/ilmedia](http://www.tu-ilmenau.de/ilmedia)  \nFrom Explainable AI to Explainable Simulation: Using Machine Learning and XAI to understand System Robustness  \nNiclas Feldkamp  \nTechnische Universität Ilmenau  \n[niclas.feldkamp@tu-ilmenau.de](niclas.feldkamp@tu-ilmenau.de)  \nABSTRACT  \nEvaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.  \nCCS CONCEPTS  \n• Computing methodologies → Modeling and simulation; Machine learning; • ;  \nKEYWORDS  \nmachine learning, deep learning, robustness optimization, simulation, explainable AI, XAI  \nACM Reference Format:  \nNiclas Feldkamp and Steffen Strassburger. 2023. From Explainable AI to Explainable Simulation: Using Machine Learning and XAI to understand System Robustness. In ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS’23), June 21–23, 2023, Orlando, FL, USA. ACM, New York, NY, USA, 11 pages. [https://doi.org/10.1145/3573900.3591114](https://doi.org/10.1145/3573900.3591114)  \nThis work is licensed under a Creative Commons Attribution International 4.0 License.  \nSIGSIM-PADS’23, June 21–23, 2023, Orlando, FL, USA © 2023 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-0030-9/23/06 .  \n[https://doi.org/10.1145/3573900.3591114](https://doi.org/10.1145/3573900.3591114)  \nSteffen Strassburger  \nTechnische Universität Ilmenau [steffen.strassburger@tu-ilmenau.de](steffen.strassburger@tu-ilmenau.de)  \n1 INTRODUCTION  \nModeling and simulation are well-established methods for the analysis of systems. Various objectives can be targeted when simulation experiments are conducted and subsequently analyzed. One of those goals is the evaluation of robustness. Robustness is particularly important for example for production and logistic systems, but the basic concept is ","cbCairsvI47WgLkx","https://ap.wps.com/l/cbCairsvI47WgLkx","pdf",4150890,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What does robustness mean in the simulation context described?\",\"answer\":\"Robustness means adjusting controllable factors so that variance from uncontrollable noise has minimal impact on the variance of the desired output.\"},{\"question\":\"How does data farming support studying system robustness?\",\"answer\":\"Data farming enables exploring large design spaces with smart experiment design, high-performance computing, automated analysis, and interactive visualization, generating data to learn system relationships.\"},{\"question\":\"Why is explainable AI needed when using machine learning for simulation analysis?\",\"answer\":\"Modern machine learning methods like neural networks and random forests are often opaque black boxes, making it difficult to analyze modeled relationships; XAI helps interpret and explore them.\"}]","From Explainable AI to Explainable Simulation - Using Machine Learning and XAI to understand System Robustness | PDF",1785734457,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"from-explainable-ai-to-explainable-simulation-using-machine-learning-and-xai-to-understand-system-robustness","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/from-explainable-ai-to-explainable-simulation-using-machine-learning-and-xai-to-understand-system-robustness/121230/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does robustness mean in the simulation context described?","Question",{"text":75,"@type":76},"Robustness means adjusting controllable factors so that variance from uncontrollable noise has minimal impact on the variance of the desired output.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does data farming support studying system robustness?",{"text":80,"@type":76},"Data farming enables exploring large design spaces with smart experiment design, high-performance computing, automated analysis, and interactive visualization, generating data to learn system relationships.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is explainable AI needed when using machine learning for simulation analysis?",{"text":84,"@type":76},"Modern machine learning methods like neural networks and random forests are often opaque black boxes, making it difficult to analyze modeled relationships; 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