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Internet Things"],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    A modern data center is supported by an Internet of Assets (IoA), a specialized Internet of Things (IoT), where the sim-ready assets encompass physical properties and connected data streams for passive data collection and proactive simulation. A digital twin (DT) is a virtual replica of the IoA system with a proper level of abstraction, integrating asset-level dynamics models to simulate system-wide behavior, prototype designs, and conduct what-if analysis. However, creating high-fidelity DTs is hindered by the manual effort needed to encode complex geometric layouts and domain-specific design constraints. While large language models (LLMs) offer automation potential, existing methods struggle to generate geometrically plausible and functionally valid DT scenes due to limited domain integration. To bridge the gaps, we propose ChatDC, a conversational system that leverages LLMs to automate data center digital twin generation through a\n                    <jats:underline>S<\/jats:underline>\n                    egment-\n                    <jats:underline>G<\/jats:underline>\n                    enerate-\n                    <jats:underline>O<\/jats:underline>\n                    ptimize (SGO) workflow. ChatDC integrates domain knowledge via a dedicated code library named DCBuild and employs SGO to decompose user prompts, generate initial structures, and optimize layouts in compliance with data center design constraints. Evaluation shows that ChatDC outperforms other baselines with 98% success rate on scratch generation tasks and reduces the average makespan by 10\u00d7. Ablation study reveals that the SGO design increases the generation success rate by 65% at most. Furthermore, computational fluid dynamics simulations validate the physical plausibility, confirming the readiness of generated DTs for real-world analysis.\n                  <\/jats:p>","DOI":"10.1145\/3772078","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T11:34:44Z","timestamp":1761046484000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["ChatDC: Geometric-aware Data Center Digital Twin Generation via Large Language Models"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5278-5255","authenticated-orcid":false,"given":"Minghao","family":"Li","sequence":"first","affiliation":[{"name":"Nanyang Technological University","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0678-7460","authenticated-orcid":false,"given":"Ruihang","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5405-9890","authenticated-orcid":false,"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Jiangxi Science and Technology Normal University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5241-1552","authenticated-orcid":false,"given":"Zhaomeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-5114","authenticated-orcid":false,"given":"Yonggang","family":"Wen","sequence":"additional","affiliation":[{"name":"Nanyang Technological University","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8441-9973","authenticated-orcid":false,"given":"Rui","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5522-355X","authenticated-orcid":false,"given":"Huiwen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Global Data Solutions Limited","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4755-1207","authenticated-orcid":false,"given":"Stuart","family":"Kennedy","sequence":"additional","affiliation":[{"name":"Global Data Solutions International Limited","place":["Singapore, Singapore"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Artec 3D. 2023. 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