ModellingEnhanced Imitation Learning of Model Predictive Control through Importance WeightingarXiv — Systems and Control (eess.SY) · yesterdayModellingLimited knowledge exchange across biological subfields in agent-based modellingarXiv — Physics and Society (physics.soc-ph) · yesterdayDigital twinReasoner-checked ontology crosswalks for a national digital twinarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinDigital twin speeds up reinforcement learning for lab robotsarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinDataset for deciding between evidence gathering and action in control-system incidentsarXiv — Computational Engineering, Finance, and Science (cs.CE) · 2dDigital twinSparse stochastic twins can outperform exact digital twinsarXiv — Computational Engineering, Finance, and Science (cs.CE) · 2dModellingPluralism of rival models in network models of sciencearXiv — Multiagent Systems (cs.MA) · 2dForecastingEstimation and MPC for balancing a wheel-legged robot under unknown disturbancesarXiv — Systems and Control (eess.SY) · 2dDigital twinConsolidating dispersed DSO data into a digital twin of the distribution gridarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinIntegrating ML and uncertainty quantification for real-time decisions in digital twinsarXiv API — полнотекстовый поиск "digital twin" · 3dDigital twinBiases in end-to-end digital twins built from chained modelsarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinDistributed Bandits for Freshness-Aware Data in MEC-Enabled Digital TwinsarXiv API — полнотекстовый поиск "digital twin" · 3d
Ground-Truth-Aware Stress Testing of a Closed-Loop Smart-Building Digital Twin Under Sensor Drift and Missing Data
US & West ·
A ground-truth-aware simulation framework is introduced that separates the latent physical state from a corrupted sensing layer and compares practical control policies with an oracle controller. The model includes 20 zones, models occupancy-driven CO2 and ventilation-energy trade-offs, sensor drift, and missing data, allowing to assess the impact of sensing errors on closed-loop performance.
Why it matters
This is important as it highlights the need to account for data quality when evaluating digital twins, which affects the reliability of building control and other systems.
arXiv:2611.06622v1 Announce Type: new Abstract: The approximation of model predictive control (MPC) algorithms with neural network models has received significant attention in the power electronics research community, as a valuable tool to enable the real-time implementation of computationally…
The work defines a formal criterion for valid mappings between digital twin ontologies: coherence and conservativity of the merged ontology. It is applied to the Information Exchange Standard and thirteen ontologies anchored on BOT, Brick and SAREF4BLDG, through matching, human review, certification and reasoning with HermiT.
TwinBridge builds digital twins from 3D Gaussian splatting and physics simulation to supply policy initialization and dense visual rewards for real-world online learning of a laboratory robot. Simulator task events and geometric progress yield rewards for pretraining, distilled into a visual reward model and combined with execution history during human-in-the-loop refinement.
