Market & productsКоц: ИИ Palantir через Nebius мог получить данные россиянTechora.ru · yesterdayModellingAccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny DronesarXiv — Systems and Control (eess.SY) · 2dDigital twinA Model-Centric DevOps Architecture for DEVS-Based Digital Twin Simulation ServicesarXiv API — полнотекстовый поиск "digital twin" · 2dDigital twinPlant Equivalent Controller Realizations for Attack-Resilient Cyber-Physical SystemsarXiv — Systems and Control (eess.SY) · 2dModellingIdentifiability of Latent Space Network Models on Anisotropic Thurston GeometriesarXiv — Physics and Society (physics.soc-ph) · 2dDigital twinОпубликованы итоги форсайт-сессии «Будущее безопасности киберфизических систем в России. Перспективы и векторы развития»spbIT · 2dForecastingBaseline-improved Economic Model Predictive Control for Optimal Microgrid DispatcharXiv — Systems and Control (eess.SY) · 2dModellingStochastic Model Predictive Control under AC Power-Flow Constraints Using Generative LearningarXiv — Systems and Control (eess.SY) · 3dMarket & productsPalantir меняет метрику: вместо количества клиентов — совокупная стоимость контрактовTechora.ru · 2dModellingАтаки на агентные системы и защита данныхХабр · 3dDigital twinEmbedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for BridgesarXiv API — полнотекстовый поиск "digital twin" · 3dModellingAgentHomeID - Agent-based modelling of building stock transformation: A multi-scale framework for policy assessment and infrastructure planningarXiv — Systems and Control (eess.SY) · 3d
Deep Learning for Fast Climate Risk Assessment of Crops
US & West ·
A deep-learning framework SECSF is presented that emulates the process-based ECroPS model for maize and barley using only daily temperatures and precipitation. Trained on ERA5 data, it reproduces crop growth dynamics and harvest timing while reducing computational cost by ~10^4 times, enabling probabilistic risk assessment for large seasonal and climate ensembles.
Why it matters
Important for scalable forecasting in agriculture, where fast and accurate climate risk estimates are crucial.
Digital twin simulation models are evolved and redeployed like software, yet DEVS-based engines offer a sound formal basis with little support for versioning, automated validation, or continuous delivery in cloud-native environments, leaving model lifecycle management ad hoc in most deployments.
arXiv:2609.06636v1 Announce Type: new Abstract: Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densification, backhaul expansion, and spectrum activation.
An action-conditioned world-modeling framework for Earth-system emulation is proposed, allowing users to specify interventions and explore system responses. It reformulates simulator trajectories as supervision for controllable state-transition learning, going beyond passive forecasting. This is relevant for interactive scientific workflows and Earth-system digital twins.
