{"id":2551,"date":"2025-03-21T07:02:29","date_gmt":"2025-03-21T07:02:29","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/03\/21\/2503-15545\/"},"modified":"2025-03-21T07:02:29","modified_gmt":"2025-03-21T07:02:29","slug":"2503-15545","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/03\/21\/2503-15545\/","title":{"rendered":"Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems"},"content":{"rendered":"

Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems
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arXiv:2503.15545v1 Announce Type: cross
\nAbstract: Network reliability assessment is pivotal for ensuring the robustness of modern infrastructure systems, from power grids to communication networks. While exact reliability computation for binary-state networks is NP-hard, existing approximation methods face critical tradeoffs between accuracy, scalability, and data efficiency. This study evaluates 20 machine learning methods across three reliability regimes full range (0.0-1.0), high reliability (0.9-1.0), and ultra high reliability (0.99-1.0) to address these gaps. We demonstrate that large-scale networks with arc reliability larger than or equal to 0.9 exhibit near-unity system reliability, enabling computational simplifications. Further, we establish a dataset-scale-driven paradigm for algorithm selection: Artificial Neural Networks (ANN) excel with limited data, while Polynomial Regression (PR) achieves superior accuracy in data-rich environments. Our findings reveal ANN’s Test-MSE of 7.24E-05 at 30,000 samples and PR’s optimal performance (5.61E-05) at 40,000 samples, outperforming traditional Monte Carlo simulations. These insights provide actionable guidelines for balancing accuracy, interpretability, and computational efficiency in reliability engineering, with implications for infrastructure resilience and system optimization.<\/div>\n

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\n Wei-Chang Yeh
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Data-Driven Approximation of Binary-State Network Reliability Function: Algorithm Selection and Reliability Thresholds for Large-Scale Systems arXiv:2503.15545v1 Announce Type: cross Abstract: Network reliability assessment is pivotal for ensuring the robustness of modern infrastructure systems, from power grids to communication networks. While exact reliability computation for binary-state networks is NP-hard, existing approximation methods face critical tradeoffs between […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,450,451,112],"tags":[84,2084,2085],"class_list":["post-2551","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-cs-na","category-math-na","category-stat-ml","tag-data","tag-reliability","tag-scale"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/2551"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=2551"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/2551\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=2551"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=2551"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=2551"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}