Results 151 to 160 of about 102,975 (203)

The non‐hydrostatic option of the ECMWF global weather forecast model: Improvements for kilometre‐scale modelling

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
This study presents improvements to the non‐hydrostatic version of the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), enabling stable global simulations at 1.4‐km resolution. A systematic comparison with the hydrostatic version at resolutions from 9 to 1.4 km shows that non‐hydrostatic effects emerge in ...
Jozef Vivoda   +3 more
wiley   +1 more source

Physics‐Informed Neural Networks for Battery Degradation Prediction Under Random Walk Operations

open access: yesQuality and Reliability Engineering International, EarlyView.
ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim   +3 more
wiley   +1 more source

Synthesizing Interacting Model‐Based Optimal Control and Model‐Free Learning Approaches for Nonlinear Systems

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we consider the optimal control problem for an unknown continuous‐time nonlinear system, and present a framework that integrates model‐based and model‐free methods to solve it. Each approach offers distinct advantages: model‐based techniques provide offline synthesis and data efficiency, while model‐free procedures excel at ...
Surabhi Athalye   +2 more
wiley   +1 more source

Measured‐State Conditioned Recursive Feasibility for Stochastic Model Predictive Control

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT In this paper, we address the problem of designing stochastic model predictive control (SMPC) schemes for linear systems affected by unbounded disturbances. The contribution of the paper is rooted in a measured‐state initialization strategy. First, due to the nonzero probability of violating chance‐constraints in the case of unbounded noise ...
Mirko Fiacchini   +2 more
wiley   +1 more source

Redefining Optimal Coverage Path Planning for FLS‐Equipped AUVs With Deep Reinforcement Learning

open access: yesJournal of Field Robotics, EarlyView.
ABSTRACT Autonomous Underwater Vehicles (AUVs) have emerged as indispensable tools for a variety of subsea tasks, from habitat monitoring and seabed mapping to infrastructure inspection and mine countermeasures. A fundamental challenge in this field is Coverage Path Planning (CPP), the problem of ensuring complete and efficient area coverage.
Lorenzo Cecchi   +3 more
wiley   +1 more source

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