Digital twinML-assisted Bayesian Calibration of Accelerator Digital TwinarXiv API — полнотекстовый поиск "digital twin" · 14hDigital twinDigital Twin Testbed for Cyberattack and Defense Analysis in Hospital IT/OT EnvironmentsarXiv API — полнотекстовый поиск "digital twin" · 17hModellingGenerative Agent-based Simulation of Mobility Policy Preferences from SurveysarXiv — Computers and Society (cs.CY) · yesterdayForecastingScalable Gaussian Process with Trigonometric Features for Safe MPCarXiv — Systems and Control (eess.SY) · yesterdayForecastingTwo-Layer MPC for Sustainable Data Centers with Workload Flexibility and Heat RecoveryarXiv — Systems and Control (eess.SY) · yesterdayForecastingIterative MPC without Derivatives for Constrained Nonlinear SystemsarXiv — Systems and Control (eess.SY) · yesterdayForecastingDisturbance-adaptive MPC with Bounds on Average Constraint ViolationsarXiv — Systems and Control (eess.SY) · yesterdayDigital twinDigital Twin Degradation: Detecting Cyber Physical Attacks via Temporal InconsistenciesarXiv API — полнотекстовый поиск "digital twin" · yesterdayForecastingControl Architecture for Fast Frequency Regulation with High Inverter-Based Resource PenetrationarXiv — Systems and Control (eess.SY) · 2dDigital twinDigital Twin-Based Intrusion Detection for Vehicle Powertrain CAN BusarXiv API — полнотекстовый поиск "digital twin" · yesterdayDigital twinPRISM: Decision-Centric Predictive Sensing for Cognitive Digital Twins in 6GarXiv API — полнотекстовый поиск "digital twin" · yesterdayModellingNeural Network-Based Parameter Estimation for a Labour Market ABMarXiv — Multiagent Systems (cs.MA) · 2d
Two-Layer MPC for Sustainable Data Centers with Workload Flexibility and Heat Recovery
US & West ·
A two-layer MPC framework for data centers with PV, battery, and heat recovery is proposed. The upper layer optimizes market participation and workload scheduling, while the lower layer compensates disturbances using multi-horizon forecasts.
Why it matters
Demonstrates practical application of MPC and forecasting for energy-efficient management of large infrastructure.
The study parametrizes hard-to-measure effects and calibrates the Bmad accelerator digital twin using Bayesian methods with beam measurements at AGS Booster, BNL. Computations are accelerated with an ML emulator.
A testbed emulating hospital IT and OT infrastructure, including EHR and SCADA, with a digital twin for monitoring and experimentation, is presented. It supports controlled cyberattacks, patch evaluation, and training of defense agents, enabling safe testing of defensive mechanisms in realistic settings.
A framework converts real survey respondents into LLM agents for simulation. This approach improves realism of behavioral simulations and can be used for experiments in mobility policy.
