[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119774-en":3,"doc-seo-119774-105":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},119774,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","GPU-Accelerated Verification of Machine Learning Models for Power Systems","Computational tools for rigorously verifying large-scale machine learning (ML) models have advanced rapidly, with GPU-accelerated branch-and-bound solvers showing particular effectiveness. For safety-critical deployment in power systems, applying these routines out of the box faces key obstacles. This work reformulates power-system verification tasks into canonical solver formats, enables simultaneous multi-constraint verification via an exact ReLU-layer transformation, and introduces a dualization method for encoding power-flow equality and inequality constraints consistently. Results on data-driven security-constrained DC-OPF verification using α,β-CROWN show up to 100× speedups and greater flexibility.","GPU-Accelerated Verification of Machine Learning Models  \nfor Power Systems  \nSamuel Chevalier, Ilgiz Murzakhanov, and Spyros Chatzivasileiadis Department of Wind and Energy Systems  \nLyngby, Denmark  \n{schev, ilgmu, [spchatz](spchatz}@dtu.dk)[}](spchatz}@dtu.dk)[@dtu.dk](spchatz}@dtu.dk)  \narXiv :2306 . 106 17v2 [ cs .LG] 8 Sep 2023  \nAbstract  \nComputational tools for rigorously verifying the performance of large-scale machine learning (ML) models have progressed significantly in recent years. The most successful solvers employ highly specialized, GPU-accelerated branch and bound routines. Such tools are crucial for the successful deployment of machine learning applications in safety-critical systems, such as power systems. Despite their successes, however, barriers prevent out-of-the-box application of these routines to power system problems. This paper addresses this issue in three key ways. First, we reformulate several key power system verification problems into the canonical format utilized by modern verification solvers. Second, we enable the simultaneous verification of multiple verification problems (e.g., checking for the violation of all constraints simultaneously, and not by solving individual verification problems) . To achieve this, we introduce an exact transformation that converts a set of potential violations into a series of ReLU-based neural network layers. This allows verifiers to interpret these layers directly, and determine the “worst-case”violation in a single shot. Third, power system ML models often must be verified to satisfy power flow constraints. We propose a dualization procedure which encodes linear equality and inequality constraints (such as power balance constraints and line flow constraints) directly into the verification problem in a manner which is mathematically consistent with the specialized verification tools. To demonstrate these innovations, we verify problems associated with data-driven security constrained DC-OPF solvers. We build and test our first set of innovations using the α,β-CROWN solver, and we benchmark against Gurobi 10.0. Our contributions achieve a speedup that can exceed 100x and allow  \nhigher degrees of verification flexibility.  \nKeywords: Branch and bound, data driven modeling, DC-OPF, neural network verification, machine learning.  \n1. Introduction  \nThe ubiquity of machine learning (ML) applications has led to a surge of interest in topics related to adversarial robustness [1], performance verification [2], and safety guarantees [3, 4] . Collectively, these tools allow ML users to have a higher degree of trust in the underlying black-box models developed by learning algorithms. In order to spur the development and implementation of such tools, the EU Commission recently proposed the creation of regulations which will help assess ML risk, engender certification requirements, and enforce industry standards [5] . The National Institute of Standard and Technologies (NIST) in the US is looking to develop similar technical standards to help with ML regulation [6, 7] .  \nIn this paper, we focus on the problem of performance verification, i.e., formally verifying that a Neural Network (NN) model obeys certain input-output mapping properties. In particular, we focus on performance verification within the context of network-constrained, safety critical applications (i.e., electrical power system operation) . Performance verification tools have recently been developed for data-driven models used in a number of power system applications. Within the reinforcement learning (RL) context, authors in [8] developed analytical feasibility ellipsoids where RL solutions were guaranteed to be feasible. This work was extended in [9], where non-feasible RL solutions were projected into convex polyhedron feasibility sets. Other works have exploited the mixed integer linear programming (MILP) NN  \nreformulation proposed in [10] . In [11], NN verification procedures were introduced for th","cbCaipoZvRf05Gho","https://ap.wps.com/l/cbCaipoZvRf05Gho","pdf",552644,1,10,"English","en",105,"# Abstract\n# Introduction\n## Performance verification and safety-critical power-system context\n## Related work in power-system ML verification","[{\"question\":\"What problem does the paper address for power systems?\",\"answer\":\"It addresses barriers to applying modern GPU-accelerated neural network verification routines directly to power system verification problems, especially those tied to operational constraints.\"},{\"question\":\"How does the proposed method support simultaneous verification of multiple constraints?\",\"answer\":\"It introduces an exact transformation that converts potential violations into a sequence of ReLU-based neural network layers, enabling verifiers to compute the worst-case violation in one pass.\"},{\"question\":\"How are power flow constraints incorporated into the verification problem?\",\"answer\":\"The paper proposes a dualization procedure that encodes linear equality and inequality constraints, such as power balance and line flow constraints, in a way consistent with specialized verification tools.\"}]","GPU-Accelerated Verification of Machine Learning Models for Power Systems | PDF",1785726252,25,{"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},"gpu-accelerated-verification-of-machine-learning-models-for-power-systems","",{"@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/gpu-accelerated-verification-of-machine-learning-models-for-power-systems/119774/",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 problem does the paper address for power systems?","Question",{"text":75,"@type":76},"It addresses barriers to applying modern GPU-accelerated neural network verification routines directly to power system verification problems, especially those tied to operational constraints.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method support simultaneous verification of multiple constraints?",{"text":80,"@type":76},"It introduces an exact transformation that converts potential violations into a sequence of ReLU-based neural network layers, enabling verifiers to compute the worst-case violation in one pass.",{"name":82,"@type":73,"acceptedAnswer":83},"How are power flow constraints incorporated into the verification problem?",{"text":84,"@type":76},"The paper proposes a dualization procedure that encodes linear equality and inequality constraints, such as power balance and line flow constraints, in a way consistent with specialized verification tools.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]