Results 161 to 170 of about 4,246 (261)

Representative Random Sampling of Chemical Space. [PDF]

open access: yesJ Chem Theory Comput
Monterrubio-Chanca DJ, von Rudorff GF.
europepmc   +1 more source

Data‐Based Refinement of Parametric Uncertainty Descriptions

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT We consider dynamical systems with a linear fractional representation involving parametric uncertainties which are either constant or varying with time. Given a finite‐horizon input‐state or input‐output trajectory of such a system, we propose a numerical scheme which iteratively improves the available knowledge about the involved constant ...
Tobias Holicki, Carsten W. Scherer
wiley   +1 more source

Robust Control Design and Analysis Based on Lifting Linearization of Nonlinear Systems Under Uncertain Initial Conditions

open access: yesInternational Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT This paper presents a robust control synthesis and analysis framework for nonlinear systems with uncertain initial conditions. First, a deep learning‐based lifting approach is proposed to approximate nonlinear dynamical systems with linear parameter‐varying (LPV) state‐space models in higher‐dimensional spaces while simultaneously ...
Sourav Sinha, Mazen Farhood
wiley   +1 more source

Topological‐defect‐featured order evolution of liquid crystals

open access: yesResponsive Materials, EarlyView.
This review provides a comprehensive introduction to the recent advances in the topological‐defect‐featured order evolution of liquid crystal (LCs), including the stimulus‐driven dynamics of defects in nematic LCs, topological defect morphological transformation during phase transitions, advanced strategies for regulating and applications of ...
Jin‐Bing Wu   +4 more
wiley   +1 more source

Wall‐to‐wall Amazon forest height mapping with Planet NICFI, Aerial LiDAR, and a U‐Net regression model

open access: yesRemote Sensing in Ecology and Conservation, EarlyView.
Tree canopy height is a key indicator of forest biomass and structure, yet accurate mapping across the Amazon remains challenging. Here, we generated a canopy height map of the Amazon forest at ~4.8 m resolution using Planet NICFI imagery and a deep learning U‐Net model trained with airborne LiDAR data.
Fabien H. Wagner   +21 more
wiley   +1 more source

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