Digital twinDe-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital TwinsarXiv API — полнотекстовый поиск "digital twin" · 2dModellingSequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power ConverterarXiv — Systems and Control (eess.SY) · 3dModellingAdaptive Handling of Deadline Misses via Stochastic MPCarXiv — Systems and Control (eess.SY) · 4dModellingInstitutionalizing Trust in Data Exchange: Trust-Anchor Model of the Data Credit Guarantee Organization and Its Simulation-Based ComparisonarXiv — Computers and Society (cs.CY) · 4dDigital twinDigital Twin-Conditioned RSS-Fingerprint Localization in Cell-Free Millimeter-Wave NetworksarXiv API — полнотекстовый поиск "digital twin" · 3dModellingAI уже делает инженеров хуже? Как агентная разработка выращивает технологический зоопаркХабр · 3dDigital twinFeasibility of a Patient-Specific Digital Twin for Ventriculoperitoneal Shunt Trajectory PlanningarXiv API — полнотекстовый поиск "digital twin" · 4dModellingBayesian Optimization under Uncertainty for Training a Scale Parameter in Stochastic ModelsarXiv — Computational Engineering, Finance, and Science (cs.CE) · 4dDigital twinDecision-Making in Digital Twins: Optimization, Control, and LearningarXiv — Systems and Control (eess.SY) · 4dDigital twinBALLAST: Physics-Aware Moving Target Defense for Industrial Control Systems Using a Digital Twin Safety OraclearXiv — Systems and Control (eess.SY) · 4dDigital twinОпубликованы итоги форсайт-сессии «Будущее безопасности киберфизических систем в России. Перспективы и векторы развития»spbIT · 4dDigital twinProbabilistic Physics Digital Twin for Human-Robot InteractionarXiv API — полнотекстовый поиск "digital twin" · 4d

Scalable Gaussian Process with Trigonometric Features for Safe MPC

US & West · 18 Aug

Researchers developed DTF-GP, a finite-dimensional kernel approximation based on deterministic trigonometric features. This approach enables uniform uncertainty bounds needed for safety guarantees in learning-based MPC while scaling to large datasets.

Why it matters

Important for scalable learning-based control with safety guarantees in large-scale systems.

Original headline: Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control

Read the original: arXiv — Systems and Control (eess.SY)

Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.

Scalable Gaussian Process with Trigonometric Features for Safe MPC

Why it matters

Related

Safety-Aware Dimension Selection for Reduced-Order Model Predictive Control

A Fully Analog Implementation of Model Predictive Control with Application to Buck Converters

Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine