Digital twinDigital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control LoopsarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinLeveraging Human-In-The-Loop Demonstrations in Reinforcement Learning for Digital Twin-Driven Robot FlexibilityarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinLIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In HomesarXiv API — полнотекстовый поиск "digital twin" · 2dModellingA dynamic pore-network model with discrete microbubble transportarXiv — Computational Engineering, Finance, and Science (cs.CE) · 3dModellingFREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledgearXiv — Multiagent Systems (cs.MA) · 3dModellingCooperation, Defection, and the Commons: Reproducing an Agent-Based Model of Aksum with Three Logistic EquationsarXiv — Physics and Society (physics.soc-ph) · 3dDigital twinRuleless digital twins: declarative decision-making via standardized frameworksarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinPredictive cognitive digital twin framework for autonomous 6G networksarXiv API — полнотекстовый поиск "digital twin" · 4dDigital twinAdjoint-based calibration and optimal control of stochastic bioprocess twinsarXiv API — полнотекстовый поиск "digital twin" · 4dDigital twinStrategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical SystemsarXiv — Multiagent Systems (cs.MA) · 4dForecastingModel Predictive Control for Safety-Critical Systems Using Taylor's Theorem with Lagrange RemainderarXiv — Systems and Control (eess.SY) · 4dEnergyBenchmarking Time Series Foundation Models for Load Forecasting Under Covariate UncertaintyarXiv — Systems and Control (eess.SY) · 4d
Initialization Is Key in Federated Short-Term Load Forecasting
US & West ·
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.
Why it matters
Relevant for forecasting tasks in the energy sector where distributed data and privacy must be considered, and adaptation to customer heterogeneity is needed.
A ruleless digital twin approach is proposed where decisions are generated automatically from purely declarative user specifications instead of hand-written rules. This reduces the complexity of building and maintaining twins under dynamically changing conditions such as weather or dynamic energy pricing.
The PCDT framework closes the loop from network observation to predictive cognition and proactive resource control. Unlike reactive twins that act after performance risk appears, it aims to pre-empt degradation.
A calibration and control method is developed for a multiscale bioprocess digital twin using sparse observations, quasi-likelihood estimation and adjoint sensitivity analysis. Parameter uncertainty then feeds into policy optimization and adaptive experiment design.
