Results 111 to 120 of about 36,403 (314)
On extended eigenvalues and extended eigenvectors of truncated shift [PDF]
We give a complete description of the set of extended eigenvectors of truncated shifts defined on the model spaces K_u := H^2\ominus uH^2, in the case of u is a Blaschke product.
arxiv
On the coding of Jacobi's method of computing eigenvalues and eigenvectors of real, symmetric matrices [PDF]
F. J. Corbató
openalex +1 more source
Abstract This article addresses the cooperative output consensus tracking problem for high‐order heterogeneous multi‐agent systems via a distributed proportional‐integral‐derivative (PID)‐like control strategy and proposes two novel control methodologies for the tuning of the control gains, which do not require any assumption and/or limitation on agent
Dario Giuseppe Lui+2 more
wiley +1 more source
Water/fat separation for self‐navigated diffusion‐weighted multishot echo‐planar imaging
A model‐based, self‐navigated water/fat decomposition algorithm (MSND) for diffusion‐weighted multishot EPI is proposed in this work. This is achieved by jointly estimating physiological motion‐induced shot‐to‐shot phase variations while performing water/fat separation using chemical shift encoding.
Yiming Dong+4 more
wiley +1 more source
Disorder Solutions for Generalized Ising Model with Multispin Interaction
This study demonstrates a development of convenient formulae for obtaining the value of the free energy in the thermodynamic limit on a set of exact disorder solutions depending on four parameters for a 2D generalized Ising model in an external magnetic ...
Pavel V. Khrapov
doaj +1 more source
Eigenvectors from eigenvalues revisited [PDF]
This is a remark on a recent post by P. Denton, S. Parke, T. Tao, X.
arxiv
Algorithm 297: Eigenvalues and Eigenvectors of the symmetric system [PDF]
John C. Boothroyd
openalex +1 more source
Model Bias Identification for Bayesian Calibration of Stochastic Digital Twins of Bridges
ABSTRACT Simulation‐based digital twins must provide accurate, robust, and reliable digital representations of their physical counterparts. Therefore, quantifying the uncertainty in their predictions plays a key role in making better‐informed decisions that impact the actual system.
Daniel Andrés Arcones+3 more
wiley +1 more source
Deep learning phase‐field model for brittle fractures
Abstract We present deep learning phase‐field models for brittle fracture. A variety of physics‐informed neural networks (PINNs) techniques, for example, original PINNs, variational PINNs (VPINNs), and variational energy PINNs (VE‐PINNs) are utilized to solve brittle phase‐field problems.
Yousef Ghaffari Motlagh+2 more
wiley +1 more source
This study examines experimentally the vortex-induced vibration (VIV) of a mechanical system with two eigenmodes. A previous experiment setup was refined to enable the experiment, and was placed in a circulating water channel to submerge a movable ...
Yoshiki NISHI, Komei SAITOH
doaj +1 more source