Results 61 to 70 of about 3,075 (261)
Dynamic Mode Decomposition for Compressive System Identification [PDF]
AIAA Journal, 2020 19 pages, 11 ...
Bai, Zhe +4 more
openaire +3 more sources
International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
Current Tracking Adaptive Control of Brushless DC Motors
International Journal of Adaptive Control and Signal Processing, EarlyView.In this paper, the current tracking for Brushless Direct Current motors is approached considering uncertainty in the parameters of the motor's model. An adaptive control scheme to compensate electrical parameters uncertainty is proposed without requiring any knowledge of the mechanical parameters.
Fernanda Ramos‐García +3 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Laser wire directed energy deposition process was leveraged to fabricate large scale NiTi alloy wall. Evaluation of build‐height‐dependent tribo‐mechanical properties revealed significant differences in location specific sample characteristics. The middle region achieved optimal performance (superior wear resistance, balanced strength‐ductility ...
Hyunsuk Choi +5 more
wiley +1 more source
Predicting rate kernels via dynamic mode decomposition
The Journal of Chemical Physics, 2023 Simulating dynamics of open quantum systems is sometimes a significant challenge, despite the availability of various exact or approximate methods. Particularly when dealing with complex systems, the huge computational cost will largely limit the applicability of these methods.
Wei Liu +4 more
openaire +3 more sources
Advanced Engineering Materials, EarlyView.This article provides an overview of recent advancements in bulk processing of rare‐earth‐free hard magnetic materials. It also addresses related simulation approaches at different scales. The research on rare‐earth‐free magnetic materials has increased significantly in recent years, driven by supply chain issues, environmental and social concerns, and
Daniel Scheiber, Andrea Bachmaier
wiley +1 more source
Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases
Energies, 2020 Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or ...
Binghua Li +3 more
doaj +1 more source
Sparsity-promoting dynamic mode decomposition [PDF]
Physics of Fluids, 2014 Dynamic mode decomposition (DMD) represents an effective means for capturing the essential features of numerically or experimentally generated flow fields. In order to achieve a desirable tradeoff between the quality of approximation and the number of modes that are used to approximate the given fields, we develop a sparsity-promoting variant of the ...
Jovanovic, Mihailo R. +2 more
openaire +3 more sources
Analyzing Electronic Excitations and Exciton Binding Energies in Y6 Films
Advanced Functional Materials, EarlyView.The Y6 molecule is used for increasing the efficiency of organic solar cells. The exciton binding energy is calculated for ensembles of Y6 molecules that are representative of the typically used films. The calculations show that the excitons typically spread out over many molecules.
Sahar Javaid Akram +2 more
wiley +1 more source
On dynamic mode decomposition: Theory and applications
Journal of Computational Dynamics, 2014 Originally introduced in the fluid mechanics community, dynamic mode decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. However, existing DMD theory deals primarily with sequential time series for which the measurement dimension is much larger than the number of measurements taken.
Tu, Jonathan H. +4 more
openaire +3 more sources