A New Class of Solvable Two-dimensional Scalar Potentials for Graphene
- URL: http://arxiv.org/abs/2209.12539v2
- Date: Sun, 2 Oct 2022 04:39:17 GMT
- Title: A New Class of Solvable Two-dimensional Scalar Potentials for Graphene
- Authors: M.V.Ioffe and D.N.Nishnianidze
- Abstract summary: Solution of two-dimensional massless Dirac equation with external electrostatic potential is presented.
Solvability obtained by means of asymmetric form of SUSY intertwining relations allows to extend the class of analytically solvable two-dimensional models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the present paper, a systematic approach is presented for solution of
two-dimensional massless Dirac equation with external electrostatic potential
applied. This approach is based on the new - asymmetric - form of SUSY-like
intertwining relations. It allows to build a wide variety of pairs of
SUSY-partner external scalar potentials. If one of them is simple enough to be
solvable, its partner is also solvable although it may have a non-trivial
dependency on both coordinates. Physically, this kind of problems is related to
the description of graphene and some other materials with external potential.
Solvability obtained by means of asymmetric form of SUSY intertwining relations
allows to extend the class of analytically solvable two-dimensional models.
Related papers
- Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations [56.78271181959529]
Generalized Additive Models (GAMs) can capture non-linear relationships between variables and targets, but they cannot capture intricate feature interactions.
We propose Shape Expressions Arithmetic ( SHAREs) that fuses GAM's flexible shape functions with the complex feature interactions found in mathematical expressions.
We also design a set of rules for constructing SHAREs that guarantee transparency of the found expressions beyond the standard constraints.
arXiv Detail & Related papers (2024-04-15T13:44:01Z) - Solvable Two-dimensional Dirac Equation with Matrix Potential: Graphene
in External Electromagnetic Field [0.0]
We find analytically the solutions for a wide class of combinations of matrix and scalar external potentials.
The main tool for this progress was provided by supersymmetrical (SUSY) intertwining relations.
arXiv Detail & Related papers (2024-01-21T15:39:49Z) - Lie Point Symmetry and Physics Informed Networks [59.56218517113066]
We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function.
Our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions.
Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.
arXiv Detail & Related papers (2023-11-07T19:07:16Z) - Prepotential Approach: a unified approach to exactly, quasi-exactly, and rationally extended solvable quantal systems [0.0]
We give a brief overview of a simple and unified way, called the prepotential approach.
It treats both exact and quasi-exact solvabilities of the one-dimensional Schr"odinger equation.
We illustrate the approach by several paradigmatic examples of Hermitian and non-Hermitian Hamiltonians with real energies.
arXiv Detail & Related papers (2023-10-22T11:40:00Z) - The Schr\"odinger equation for the Rosen-Morse type potential revisited
with applications [0.0]
We rigorously solve the time-independent Schr"odinger equation for the Rosen-Morse type potential.
The resolution of this problem is used to show that the kinks of the non-linear Klein-Gordon equation with $varphi2p+2$ type potentials are stable.
arXiv Detail & Related papers (2023-04-12T18:43:39Z) - Relativistic dynamical inversion in manifestly covariant form [0.0]
The Relativistic Dynamical Inversion technique is a novel tool for finding analytical solutions to the Dirac equation.
The most remarkable feature of the new method is the ease of performing non-trivial change of reference frames.
A whole family of normalizable analytic solutions to the Dirac equation is constructed.
arXiv Detail & Related papers (2022-05-24T10:19:28Z) - Decimation technique for open quantum systems: a case study with
driven-dissipative bosonic chains [62.997667081978825]
Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics.
We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function.
We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
arXiv Detail & Related papers (2022-02-15T19:00:09Z) - Lie Point Symmetry Data Augmentation for Neural PDE Solvers [69.72427135610106]
We present a method, which can partially alleviate this problem, by improving neural PDE solver sample complexity.
In the context of PDEs, it turns out that we are able to quantitatively derive an exhaustive list of data transformations.
We show how it can easily be deployed to improve neural PDE solver sample complexity by an order of magnitude.
arXiv Detail & Related papers (2022-02-15T18:43:17Z) - Exact solutions of interacting dissipative systems via weak symmetries [77.34726150561087]
We analytically diagonalize the Liouvillian of a class Markovian dissipative systems with arbitrary strong interactions or nonlinearity.
This enables an exact description of the full dynamics and dissipative spectrum.
Our method is applicable to a variety of other systems, and could provide a powerful new tool for the study of complex driven-dissipative quantum systems.
arXiv Detail & Related papers (2021-09-27T17:45:42Z) - Bilayer graphene in magnetic fields generated by supersymmetry [0.0]
Hamiltonian for electrons in bilayer graphene with applied magnetic fields is solved through second-order supersymmetric quantum mechanics.
New kinds of magnetic fields associated to non-shape-invariant SUSY partner potentials are generated.
arXiv Detail & Related papers (2021-01-13T23:29:41Z) - Inverse Learning of Symmetries [71.62109774068064]
We learn the symmetry transformation with a model consisting of two latent subspaces.
Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.
Our model outperforms state-of-the-art methods on artificial and molecular datasets.
arXiv Detail & Related papers (2020-02-07T13:48:52Z)
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.