Digital twinForesight session outlines vectors for cyber-physical systems security development until 2040spbIT · 2hDigital twinNaver's digital twin tech wins Saudi smart city standard designationKED Global · 15hDigital twinNaver wins Saudi approval for national digital twin platformThe Korea Times · yesterdayModellingAdaptive Nonlinear MPC without Prior TrainingarXiv — Systems and Control (eess.SY) · yesterdayFood & agriDeep Learning for Fast Climate Risk Assessment of CropsarXiv — Computational Engineering, Finance, and Science (cs.CE) · yesterdayModellingParallel branch MPC on GPUsarXiv — Systems and Control (eess.SY) · 2dModellingImpact of shared autonomous vehicles in microtransit: Atlanta casearXiv — Systems and Control (eess.SY) · 2dModellingHybrid simulation of circular supply chains in healthcarearXiv — Systems and Control (eess.SY) · 2dDigital twinFDF tool simplifies building AI digital twinsarXiv API — полнотекстовый поиск "digital twin" · 2dForecastingMPC and genetic algorithm for flying pendulum controlarXiv — Systems and Control (eess.SY) · 2dForecastingHybrid control architecture for safe vehicle lateral controlarXiv — Systems and Control (eess.SY) · 2dForecastingAdaptive MPC for ground vehicles: review and implementationarXiv — Systems and Control (eess.SY) · 2d
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.
A GPU-based solver for trajectory planning problems using branch model predictive control is presented. The solver is based on iterative LQR, multiple-shooting formulation, and augmented Lagrangian method for constraint handling. Numerical experiments show superiority over a CPU-based solver on large-scale problems.
The integration of shared autonomous vehicles (SAVs) into microtransit systems, which address the last-mile problem, is investigated. The Atlanta case study shows that SAVs can improve sustainability, convenience, and reliability compared to conventional fixed-route transit.
Function+Data Flow (FDF), a visual domain-specific language for specifying and validating AI/ML pipelines used in building real-time digital twins, is presented. Implemented in DesCartes Builder, it enables composition and reuse of models. An empirical study found FDF makes AI-based twin development more accessible and reliable.
