Digital twinHow Much Training is Needed with a Digital Twin?arXiv API — полнотекстовый поиск "digital twin" · 16hDigital twinPUR-1 Cyber-Physical Digital TwinarXiv — Computational Engineering, Finance, and Science (cs.CE) · yesterdayDigital twinForesight session on the future of cyber-physical systems security in RussiaspbIT · yesterdayForecastingStochastic Nonlinear MPC with Gaussian Mixture Uncertainty PropagationarXiv — Systems and Control (eess.SY) · yesterdayMarket & productsUS Nurses Protest Against Hospital Contracts with PalantirCNews · yesterdayModellingDigital Sector Expands AI Practice with Agentic ScenariosCNews · 2dModellingMechanics of Democratic Dominance: System Dynamics for Consent EngineeringarXiv — Physics and Society (physics.soc-ph) · 2dDigital twinRemote-Sensing-Based Channel Modeling for 6G Digital TwinsarXiv API — полнотекстовый поиск "digital twin" · 2dForecastingDistributed MPC for Optimal Consensus of Heterogeneous Multi-Agent SystemsarXiv — Systems and Control (eess.SY) · 2dEnergyInitialization Is Key in Federated Short-Term Load ForecastingarXiv — Systems and Control (eess.SY) · 2dDigital twinSpec2Twin-Chain: Automating Blockchain Digital Twin ConstructionarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinCyber-Physical Machine Tool with Real-Time Machining Process Digital TwinarXiv API — полнотекстовый поиск "digital twin" · 2d
The article discusses an architecture of a digital twin for nuclear systems, integrating multiple models (physics-based and data-driven) to support decision making, state estimation, predictive control, and real-time data processing. The twin must synchronize with the physical facility faster than its operational cycle.
Why it matters
Important for modeling and management of complex systems as it demonstrates requirements for digital twins in critical infrastructure.
Investigates how many training pilots are needed when a digital twin of a wireless channel is used for channel estimation. The twin is treated as a complementary measurement fused with pilot observations. For the first time, this trade-off is formalized and quantified.
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.
A new stochastic nonlinear model predictive control method for systems with additive noise is proposed. State distribution is approximated by Gaussian mixture with error bounds in Wasserstein distance. This yields closed-form expressions for expected costs and chance constraints, and the problem is solvable via nonlinear programming with correctness guarantees.
