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1 er of principal components (eigen-vectors or eigenfunctions).
2  available for further analyses as secondary eigenfunctions.
3                              The data-driven eigenfunctions allow a flexible and parsimonious represe
4 ollective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator.
5 n terms of the isotropic harmonic oscillator eigenfunctions and Hydrogen atom eigenfunctions independ
6 irm the dominant spectral frequencies of the eigenfunctions and locate them as (absolute valued) modu
7                      Hydrophobic free energy eigenfunctions and their transformations offer new quant
8 e hydrophobic free energy sequences or their eigenfunctions; and (iii) discrete, best bases, trigonom
9                                          The eigenfunctions are expressed in terms of Heun confluent
10 ortex knots occur frequently, even in random eigenfunctions at relatively low energy, and are constra
11  advantages of our approach which uses fewer eigenfunctions/base functions.
12 echniques to address these difficulties: (i) eigenfunction construction from the linear decomposition
13 lex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation an
14  distribution is derived using the method of eigenfunction expansion.
15  this property by computing the ground-state eigenfunction for a simplified Schrodinger operator with
16 "local" projection is made using the leading eigenfunction for the MPB density equation.
17 ey can be used to obtain the eigenvalues and eigenfunctions for the same potential, orientated at an
18 odes approximate the eigenvalue spectrum and eigenfunctions in a systematically improvable manner, an
19                                The number of eigenfunctions in the classifier can be chosen by leave-
20  oscillator eigenfunctions and Hydrogen atom eigenfunctions independently, showing that each one resu
21 ical evidence to support the conjecture that eigenfunctions inherit this property by computing the gr
22 of the leading eigenvector-derived, m(1)AChR eigenfunctions locates seven hydrophobic transmembrane s
23                                  The leading eigenfunction of the covariance matrix of a transmembran
24  The function [Formula: see text] (x) is the eigenfunction of the Kolmogorov backward operator with t
25  method explicitly finds the eigenvalues and eigenfunctions of the diffusion generator associated wit
26 equilibrium averages and the eigenvalues and eigenfunctions of the diffusion operator.
27  kernel and its gradient, as well as for the eigenfunctions of the Laplacian and their gradient, that
28                       We use heat kernels or eigenfunctions of the Laplacian to construct local coord
29     The spectral method exploits the natural eigenfunctions of the master equation of birth-death pro
30  Hermite functions," a generalization of the eigenfunctions of the quantum harmonic oscillator and th
31                  For the symmetric case, the eigenfunctions of the supercritical states are expressed
32 tem in terms of the ordinary and generalized eigenfunctions of the underlying Hamiltonian.
33          We show that appropriately selected eigenfunctions or scaling functions of Markov matrices,
34     Constructions of hydrophobic free energy eigenfunctions, psil, from M-lagged, M x M autocovarianc
35 alized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then ac
36                            In these receptor eigenfunctions, we find a set of statistical wavelength
37 which allows us to obtain analytically exact eigenfunctions whose ergodicity-breaking properties can