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
Operator Learning for Transcranial Focused Ultrasound Digital Twins
US & West ·
A new approach to modeling transcranial focused ultrasound based on operator learning is proposed: a coordinate-aware neural operator tFUSOperator that maps free-field pressure, skull anatomy, and treatment parameters to the intracranial field in a shared physical coordinate frame. This is the first operator-based formulation for tFUS field prediction.
Why it matters
Demonstrates the potential of operator learning for fast and accurate digital twins in medical applications, which could be adapted for other physical systems.
Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intelligent applications. In this context, Digital Twins (DTs) have emerged as a key technology for system analysis and optimization.
Growing automation makes collaborative robots work in more variable environments, increasing the need for adaptation. We propose a human-in-the-loop online training framework combining a digital twin (DT), reinforcement learning (RL), and human demonstrations.
Realistic household simulation must capture not only diverse environments but also the lived-in object arrangements and spatial constraints that shape robot motion and interaction. Existing resources often trade off scale, real-world correspondence, and interaction readiness, leaving a gap in…
