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
Sparse stochastic twins can outperform exact digital twins
US & West ·
The study shows that keeping weakly dependent or auxiliary degrees of freedom raises the dimensionality of learning and control problems, so a sparse stochastic surrogate can beat an exact high-dimensional model. It introduces a method for dependence discovery at the level of the full conditional law, combining conditional generative modeling, Gaussian-process distillation and kernel mode decomposition.
Why it matters
It challenges the assumption that higher model fidelity always helps and offers a way to select a twin's degrees of freedom for a given control task.
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…
Cardiac digital twins (CDTs) provide a computational platform for personalized cardiac modeling, but their predictive behavior depends strongly on the selected ionic model and the representation of transmural electrophysiological heterogeneity.
