Digital twinDe-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital TwinsarXiv API — полнотекстовый поиск "digital twin" · 4dModellingSequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power ConverterarXiv — Systems and Control (eess.SY) · 5dModellingAdaptive Handling of Deadline Misses via Stochastic MPCarXiv — Systems and Control (eess.SY) · 6dModellingInstitutionalizing Trust in Data Exchange: Trust-Anchor Model of the Data Credit Guarantee Organization and Its Simulation-Based ComparisonarXiv — Computers and Society (cs.CY) · 6dDigital twinDigital Twin-Conditioned RSS-Fingerprint Localization in Cell-Free Millimeter-Wave NetworksarXiv API — полнотекстовый поиск "digital twin" · 5dModellingAI уже делает инженеров хуже? Как агентная разработка выращивает технологический зоопаркХабр · 5dDigital twinFeasibility of a Patient-Specific Digital Twin for Ventriculoperitoneal Shunt Trajectory PlanningarXiv API — полнотекстовый поиск "digital twin" · 5dModellingBayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic ModelsarXiv — Computational Engineering, Finance, and Science (cs.CE) · 6dDigital twinDecision-Making in Digital Twins: Optimization, Control, and LearningarXiv — Systems and Control (eess.SY) · 6dDigital twinBALLAST: Physics-Aware Moving Target Defense for Industrial Control Systems Using a Digital Twin Safety OraclearXiv — Systems and Control (eess.SY) · 6dDigital twinОпубликованы итоги форсайт-сессии «Будущее безопасности киберфизических систем в России. Перспективы и векторы развития»spbIT · 6dDigital twinProbabilistic Physics Digital Twin for Human-Robot InteractionarXiv API — полнотекстовый поиск "digital twin" · 6d
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 twins (DTs) can generate dense synthetic fingerprints for localization via ray tracing, yet the sim-to-real domain gap limits their direct use, particularly in cell-free millimeter-wave (mmWave) deployments where distributed geometries and blockage-induced sparsity widen the mismatch.
Evaluating 5G positioning on a physical testbed ties every measurement to one propagation environment and one antenna layout, so any question about an alternative deployment requires a new field campaign.
A ruleless digital twin approach is proposed where decisions are generated automatically from purely declarative user specifications instead of hand-written rules. This reduces the complexity of building and maintaining twins under dynamically changing conditions such as weather or dynamic energy pricing.
