Anomalous bulk-edge correspondence of nonlinear Rice-Mele model
- URL: http://arxiv.org/abs/2501.02478v1
- Date: Sun, 05 Jan 2025 08:30:37 GMT
- Title: Anomalous bulk-edge correspondence of nonlinear Rice-Mele model
- Authors: Chenxi Bai, Zhaoxin Liang,
- Abstract summary: This work aims to extend Isobe's analysis to uncover BEC of eigenvalue's nonlinearity in intrinsic nonlinear Hamiltonians.
We numerically solve the nonlinear Rice-Mele (RM) model and identify two distinct types of nonlinear eigenvalues.
We establish a novel form of BEC based on these auxiliary nonlinear eigenvalues, which we term the anomalous BEC of a nonlinear physical system.
- Score: 0.0
- License:
- Abstract: Bulk-edge correspondence (BEC) constitutes a fundamental concept within the domain of topological physics, elucidating the profound interplay between the topological invariants that characterize the bulk states and the emergent edge states. A recent highlight along this research line consists of establishing BEC under the eigenvalue's nonlinearity in a linear Hamiltonian by introducing auxiliary eigenvalues [\href{https://doi.org/10.1103/PhysRevLett.132.126601}{ T. Isobe {\it et al.,} Phys. Rev. Lett. 132, 126601 (2024)}]. The purpose of this work aims to extend Isobe's analysis to uncover BEC of eigenvalue's nonlinearity in intrinsic nonlinear Hamiltonians. To achieve this, we numerically solve the nonlinear Rice-Mele (RM) model and identify two distinct types of nonlinear eigenvalues: the intrinsically nonlinear eigenvalues and the eigenvalue's nonlinearity introduced through the incorporation of auxiliary eigenvalues. Furthermore, we establish a novel form of BEC based on these auxiliary nonlinear eigenvalues, which we term the anomalous BEC of a nonlinear physical system. The concept of the anomalous BEC defined herein provides a novel perspective on the intricate interplay between topology and nonlinearity in the context of BEC.
Related papers
- Kernel Methods for the Approximation of the Eigenfunctions of the Koopman Operator [1.7702475609045947]
We introduce a kernel-based method to construct the principal eigenfunctions of the Koopman operator without explicitly computing the operator itself.
We exploit the structure of the principal eigenfunctions by decomposing them into linear and nonlinear components.
arXiv Detail & Related papers (2024-12-21T11:25:51Z) - Exceptional Points and Stability in Nonlinear Models of Population Dynamics having $\mathcal{PT}$ symmetry [49.1574468325115]
We analyze models governed by the replicator equation of evolutionary game theory and related Lotka-Volterra systems of population dynamics.
We study the emergence of exceptional points in two cases: (a) when the governing symmetry properties are tied to global properties of the models, and (b) when these symmetries emerge locally around stationary states.
arXiv Detail & Related papers (2024-11-19T02:15:59Z) - Exceptional points and non-Hermitian skin effects under nonlinearity of eigenvalues [0.0]
nonlinear systems may exhibit exceptional points and non-Hermitian skin effects which are unique non-Hermitian topological phenomena.
Our analysis elucidates that exceptional points may emerge even for systems without an internal degree of freedom where the equation is single component.
arXiv Detail & Related papers (2024-07-30T15:15:39Z) - Adjusting exceptional points using saturable nonlinearities [0.0]
We study the impact of saturable nonlinearity on the presence and location of exceptional points in a non-Hermitian dimer system.
To identify the exceptional points, we calculate the nonlinear eigenvalues both from the equation for the defined population imbalance and through a fully numerical method.
arXiv Detail & Related papers (2024-02-24T11:40:23Z) - Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification [72.77513633290056]
We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model.
Our method captures intricate patterns and relationships, enhancing classification performance.
arXiv Detail & Related papers (2024-02-14T16:10:42Z) - Nonlinearity enabled higher-dimensional exceptional topology [2.132096006921048]
We show that nonlinearity plays a crucial role in forming topological singularities of non-Hermitian systems.
Our findings lead to advances in the fundamental understanding of the peculiar topology of nonlinear non-Hermitian systems.
arXiv Detail & Related papers (2022-07-14T02:44:53Z) - On the Identifiability of Nonlinear ICA: Sparsity and Beyond [20.644375143901488]
How to make the nonlinear ICA model identifiable up to certain trivial indeterminacies is a long-standing problem in unsupervised learning.
Recent breakthroughs reformulate the standard independence assumption of sources as conditional independence given some auxiliary variables.
We show that under specific instantiations of such constraints, the independent latent sources can be identified from their nonlinear mixtures up to a permutation.
arXiv Detail & Related papers (2022-06-15T18:24:22Z) - Hessian Eigenspectra of More Realistic Nonlinear Models [73.31363313577941]
We make a emphprecise characterization of the Hessian eigenspectra for a broad family of nonlinear models.
Our analysis takes a step forward to identify the origin of many striking features observed in more complex machine learning models.
arXiv Detail & Related papers (2021-03-02T06:59:52Z) - Sparse Quantized Spectral Clustering [85.77233010209368]
We exploit tools from random matrix theory to make precise statements about how the eigenspectrum of a matrix changes under such nonlinear transformations.
We show that very little change occurs in the informative eigenstructure even under drastic sparsification/quantization.
arXiv Detail & Related papers (2020-10-03T15:58:07Z) - Eigendecomposition-Free Training of Deep Networks for Linear
Least-Square Problems [107.3868459697569]
We introduce an eigendecomposition-free approach to training a deep network.
We show that our approach is much more robust than explicit differentiation of the eigendecomposition.
Our method has better convergence properties and yields state-of-the-art results.
arXiv Detail & Related papers (2020-04-15T04:29:34Z) - WICA: nonlinear weighted ICA [72.02008296553318]
Independent Component Analysis (ICA) aims to find a coordinate system in which the components of the data are independent.
We construct a new nonlinear ICA model, called WICA, which obtains better and more stable results than other algorithms.
arXiv Detail & Related papers (2020-01-13T10:38:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.