Digital twinSchneider Electric, AVEVA, Nscale and Start Campus pilot AI digital twinEngineering.com · 2dDigital twinLarge Language Models Meet Bayesian Optimization: Towards Self-evolving Wireless Digital TwinsarXiv API — полнотекстовый поиск "digital twin" · 2dModellingJaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in SecondsarXiv — General Economics (econ.GN) · 2dDigital twinDigital Twins of Networked Dynamical Systems: Machine Learning Prediction of Synchronization Transitions in Coupled Chaotic OscillatorsarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinEvaluating Type-1 Diabetes Digital Twins by Decision QualityarXiv API — полнотекстовый поиск "digital twin" · 2dForecastingCertified Neural Approximation of Robust Nonlinear Model Predictive ControlarXiv — Systems and Control (eess.SY) · 2dForecastingIntegrating Flow Matching and Model Predictive Control for Safe Robot NavigationarXiv — Systems and Control (eess.SY) · 2dForecastingRegion of Attraction Estimation for Linear Quadratic Regulator, Linear and Robust Model Predictive Control on a Two-Wheeled Inverted PendulumarXiv — Systems and Control (eess.SY) · 2dDigital twinVivix Executable Digital Twin for Float GlassSnowflake · 2dDigital twinMicrostructural Generative Inverse Models for Additive Manufacturing Digital TwinsarXiv API — полнотекстовый поиск "digital twin" · 2dModellingCorporate AI in Russia: the bet on agent platformsTAdviser (Telegram) · 3dModellingCorporate AI in Russia: market growth amid unfinished pilotsTAdviser · 3d
AgentHomeID - Agent-based modelling of building stock transformation: A multi-scale framework for policy assessment and infrastructure planning
US & West ·
arXiv:2609.05763v1 Announce Type: new Abstract: Decarbonising the building sector is central to meeting climate targets, yet existing models rarely capture the interaction between system-level transformation dynamics and heterogeneous individual investment decisions.
arXiv:2610.28907v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) offers a principled way of populating simulated economies with many heterogeneous decision makers.
arXiv:2604.04455v3 Announce Type: replace Abstract: Nonlinear underactuated systems such as two-wheeled inverted pendulums (TWIPs) exhibit a limited region of attraction (RoA), which defines the set of initial conditions from which the closed-loop system converges to the equilibrium.
The same TAdviser item: the generative AI market reached 58 billion rubles in 2025, with 44 billion corporate. Measurable results are becoming the key criterion, while most projects remain pilots due to integration, data quality, security and cost issues.
