Digital twinJanus: One Agent, Two Roles for Digital Twin Construction and Network OrchestrationarXiv API — полнотекстовый поиск "digital twin" · 3dModellingStochastic MPC robust to errors in disturbance statisticsarXiv — Systems and Control (eess.SY) · 3dModellingРоман Венедиктов («Эмерджентные мультиагентные системы»): «Сегодня сам факт использования ИИ стал обычной частью корпоративной ИТ-среды»spbIT · 3dDigital twinA High-Fidelity Cardiac Digital Twin for Assessing Rate-Dependent Responses: A Comparative Multi-scale Study of TT2 and Tomek Models in Stratified 3D VentriclesarXiv API — полнотекстовый поиск "digital twin" · 3dForecastingAdaptive clustering for robust distributed MPC of coupled multi-agent systemsarXiv — Systems and Control (eess.SY) · 3dForecastingNeural approximation of NMPC without expert demonstrationsarXiv — Systems and Control (eess.SY) · 3dAssets & dataAn operational model for a trusted data space in vocational educationarXiv — Computers and Society (cs.CY) · 3dModellingАгентная система для тех, кто вкатывается: собираем агента поддержки от одного файла до Temporal, саги и circuit breakerХабр · 4dDigital twinOnline digital twin: data assimilation and control in a mean-field formulationarXiv — Systems and Control (eess.SY) · 4dDigital twinSurvey of digital twin networks for 6G: architecture, technologies and AI controlarXiv — Systems and Control (eess.SY) · 4dDigital twin5G NR neural receiver trained on corridor digital twin dataarXiv API — полнотекстовый поиск "digital twin" · 4dForecastingSampling-based MPC unified as a single sequential Monte Carlo algorithmarXiv — Systems and Control (eess.SY) · 4d
Dataset for deciding between evidence gathering and action in control-system incidents
US & West ·
The authors present TE-GvA, a counterfactual decision dataset for fault-driven incident response on the revised Tennessee Eastman process: 9,908 decision points from 1,990 simulated incidents, each with 4–35 candidate actions labelled via branched rollouts. With labels computed under the true hidden cause, gathering evidence is the best choice in only 1.1–1.3% of decisions.
Why it matters
It shows how to train and evaluate control decisions under uncertainty about fault cause, where the cost of delay rivals the cost of a wrong action.
Next-generation wireless networks are increasingly large-scale, heterogeneous, and dynamic, posing significant challenges to network modeling and control. Digital twins offer a promising solution, but existing approaches typically rely on static models and decouple twin construction from network…
Совместное предприятие Selectel и ИТМО «Эмерджентные мультиангентные системы» занимается технологиями для создания и эксплуатации мультиагентных систем: не столько созданием ещё одного отдельного ИИ-ассистента, сколько тем, как сделать эксплуатацию большого количества агентов управляемой, надежной…
