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) · 2dModellingFREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledgearXiv — Multiagent Systems (cs.MA) · 2dModellingCooperation, Defection, and the Commons: Reproducing an Agent-Based Model of Aksum with Three Logistic EquationsarXiv — Physics and Society (physics.soc-ph) · 2dDigital 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" · 3dDigital twinAdjoint-based calibration and optimal control of stochastic bioprocess twinsarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinStrategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical SystemsarXiv — Multiagent Systems (cs.MA) · 3dForecastingModel Predictive Control for Safety-Critical Systems Using Taylor's Theorem with Lagrange RemainderarXiv — Systems and Control (eess.SY) · 3dEnergyBenchmarking Time Series Foundation Models for Load Forecasting Under Covariate UncertaintyarXiv — Systems and Control (eess.SY) · 3d
Linearly Scalable Nonlinear MPC with Adaptive Horizons
US & West ·
Observed control leverages the duality between state estimation and model predictive control to compute control actions with linear scalability in prediction horizon length. The algorithms provide adaptive horizon lengths and early termination criteria, using Kalman smoothers as the backend. A separate formulation splits linear MPC into purely reactive and anticipatory components.
Why it matters
Linear horizon scalability and any-time operation are important for real-time management of large-scale systems.
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.
