Digital twinForesight session on the future of cyber-physical systems security in RussiaspbIT · 2hMarket & productsUS Nurses Protest Against Hospital Contracts with PalantirCNews · 20hModellingDigital Sector Expands AI Practice with Agentic ScenariosCNews · yesterdayModellingMechanics of Democratic Dominance: System Dynamics for Consent EngineeringarXiv — Physics and Society (physics.soc-ph) · yesterdayForecastingDistributed MPC for Optimal Consensus of Heterogeneous Multi-Agent SystemsarXiv — Systems and Control (eess.SY) · yesterdayEnergyInitialization Is Key in Federated Short-Term Load ForecastingarXiv — Systems and Control (eess.SY) · yesterdayDigital twinCOMSOL Continues Integration of Multiphysics Simulation and Digital Twins搜狐网 · 3dDigital twinVerification of Composed Digital Twins in Systems-of-SystemsarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinCOMSOL showcases multiphysics simulation for industrial digital twins中华网生活 · 4dDigital twinA Trans-Domain Digital Twin for Bio-Aware Climate and Energy Control in Cattle BarnsarXiv API — полнотекстовый поиск "digital twin" · 4dDigital twinDINIRS: Digital Twin for Individualized Treatment Effects of Respiratory SupportarXiv API — полнотекстовый поиск "digital twin" · 4dDigital twinMathWorks Unveils Digital Twin Workflow for Radar and Satellite Communicationst.cj.sina.cn · 6d
AI and digital twins disrupt glass fiber manufacturing: predictive maintenance and a $6 billion deal
Africa ·
AI and digital twins are being implemented in glass fiber manufacturing for predictive maintenance. A major $6 billion infrastructure deal signals large-scale industry modernization.
Why it matters
The news is relevant to industrial process modeling: the use of digital twins and predictive maintenance improves efficiency and reliability of production.
The company 'Aktiv' has finalized a document based on the foresight session held in Moscow in March 2025. The document includes strategic recommendations and forecasts for the industry development until 2040.
The study focuses on federated learning for short-term load forecasting (STLF), addressing data privacy concerns. The authors identify structured heterogeneity in clients' load data: different responses to exogenous factors and distinct temporal load profiles, which degrade forecasting performance in federated learning. To mitigate these issues, they propose two model initialization strategies — global and local — that improve forecasting accuracy.
COMSOL is advancing integration of multiphysics simulation with digital twin technologies. This enables more accurate and comprehensive models for industrial and engineering applications.
