Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods

From speculation to reality: Enhancing anticipatory ethics for emerging technologies (ATE) in practice

Abstracting the Traffic of Nonlinear Event-Triggered Control Systems

Capturing the uncertainty about a sudden change in the properties of time series with confidence curves

Modeling Pedestrian Tactical and Operational Decisions Under Risk and Uncertainty: A Two-Layer Model Framework

Impact of Leader-Follower Behavior on Evacuation Performance: An Exploratory Modeling Approach

Real-Time Train Scheduling With Uncertain Passenger Flows: A Scenario-Based Distributed Model Predictive Control Approach

Promises of Deep Kernel Learning for Control Synthesis

Formal Abstraction of General Stochastic Systems via Noise Partitioning

Adaptive Prescribed Performance Asymptotic Tracking for High-Order Odd-Rational-Power Nonlinear Systems

Robustness Analysis of Platoon Control for Mixed Types of Vehicles

Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation

Use of confidence curves to represent uncertainty in hydro-meteorological time series analysis

Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids

Technological Uncertainty and Standardization Strategies: A Coopetition Framework

Online Multi-Robot Task Assignment with Stochastic Blockages

Distributionally Robust Optimization via Haar Wavelet Ambiguity Sets

Uncertain uncertainty in data-driven stochastic optimization: towards structured ambiguity sets

On Learning Discrete-Time Fractional-Order Dynamical Systems

Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning