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In this paper we present a general method to solve non hermetic potentials with PT symmetry using the introduction of two first-order operator against {\eta}-pseudo-hermetic({\eta}-weak-pseudo-hermiticity) with position dependent effective…

Quantum Physics · Physics 2019-09-11 Fereshte Soliemani , Zahra Bakhshi

The Koopman Operator (KO) is a mathematical construct that maps nonlinear (state space) dynamics to corresponding linear dynamics in an infinite-dimensional functional space. For practical applications, finite-dimensional approximations can…

Dynamical Systems · Mathematics 2025-06-19 Craig Bakker

A complexified von Roos Hamiltonian is considered and a Hermitian first-order intertwining differential operator is used to obtain the related position dependent mass $\eta$-weak-pseudo-Hermitian Hamiltonians. Using a Liouvillean-type…

Quantum Physics · Physics 2009-11-13 Omar Mustafa , S. Habib Mazharimousavi

We consider minisuperspace models with two-derivative kinetic terms, assuming a flat target space and a closed Universe. We show that, upon canonical quantization of the Hamiltonian, only a restricted class of operator orderings is…

High Energy Physics - Theory · Physics 2026-05-19 Victor Franken , Eftychios Kaimakkamis , Hervé Partouche , Nicolaos Toumbas

Let $\mathcal{M}$ be an atomless semifinite von Neumann algebra (or an atomic von Neumann algebra with all atoms having the same trace) acting on a (not necessarily separable) Hilbert space $H$ equipped with a semifinite faithful normal…

Operator Algebras · Mathematics 2023-01-09 Jinghao Huang , Fedor Sukochev

A new quantum mechanical notion -- Conditional Density Matrix -- is discussed and is applied to describe some physical processes. This notion is a natural generalization of von Neumann density matrix for such processes as divisions of…

Quantum Physics · Physics 2007-05-23 V. V. Belokurov , O. A. Khrustalev , V. A. Sadovnichy , O. D. Timofeevskaya

The concept of energy-dependent forces in quantum mechanics is re-analysed. We suggest a simplification of their study via the representation of each self-adjoint and energy-dependent Hamiltonian H=H(E) with real spectrum by an auxiliary…

Quantum Physics · Physics 2007-05-23 Miloslav Znojil , Hynek Bila , Vit Jakubsky

A gradient dependent formula is derived for the spinless one-particle density-matrix operator z from the differential virial theorem. A gradient dependent formula is also derived for a spinless one-particle density-matrix operator that can…

Chemical Physics · Physics 2025-01-31 James P. Finley

This work introduces non-Hermitian position-dependent mass Hamiltonians characterized by complex ladder operators and real, equidistant spectra. By imposing the Heisenberg-Weyl algebraic structure as a constraint, we derive the…

Mathematical Physics · Physics 2025-08-14 M. I. Estrada-Delgado , Z. Blanco-Garcia

The energy eigenvalues of the quantum particle constrained in a surface of the sphere of D dimensions embedded in a $R^{D+1}$ space are obtained by using two different procedures: in the first, we derive the Hamiltonian operator by squaring…

High Energy Physics - Theory · Physics 2007-05-23 Jorge Ananias Neto , Wilson Oliveira

The Distributional Koopman Operator (DKO) is introduced as a way to perform Koopman analysis on random dynamical systems where only aggregate distribution data is available, thereby eliminating the need for particle tracking or detailed…

Dynamical Systems · Mathematics 2025-04-17 Maria Oprea , Alex Townsend , Yunan Yang

The concept of quantum-mechanical nematic order, which is important in systems such as superconductors, is based on an analogy to classical liquid crystals, where order parameters are obtained through orientational expansions. We generalize…

Quantum Physics · Physics 2020-12-23 Michael te Vrugt , Raphael Wittkowski

Discovering hidden physical laws and identifying governing system parameters from sparse observations are central challenges in computational science and engineering. Existing data-driven methods, such as physics-informed neural networks…

Machine Learning · Computer Science 2026-04-16 Dibakar Roy Sarkar , Vijay Kag , Birupaksha Pal , Somdatta Goswami

We introduce a family of relativistic non-rigid non-inertial frames as a gauge fixing of the description of N positive energy particles in the framework of parametrized Minkowski theories. Then we define a multi-temporal quantization scheme…

High Energy Physics - Theory · Physics 2009-11-11 David Alba , Luca Lusanna

The Bohmian formulation of quantum mechanics is used in order to describe the measurement process in an intuitive way without a reduction postulate in the framework of a deterministic single system theory. Thereby the motion of the hidden…

Quantum Physics · Physics 2007-05-23 H. Geiger , G. Obermair , Ch. Helm

The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks (DNNs) for learning such a mapping, enabling the learning…

Machine Learning · Statistics 2024-02-15 Yusuke Tanaka , Takaharu Yaguchi , Tomoharu Iwata , Naonori Ueda

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize…

Machine Learning · Computer Science 2021-12-10 Gabriel S. Gusmão , Adhika P. Retnanto , Shashwati C. da Cunha , Andrew J. Medford

This paper derives and demonstrates a new, purely density-based ab initio approach for calculation of the energies and properties of many-electron systems. It is based upon the discovery of relationships that govern the "mechanics" of the…

Chemical Physics · Physics 2024-09-04 James C. Ellenbogen

We consider the Hamiltonian $H$ of a particle in one dimension with a position dependent mass for which we apply the recent strategy of the so-called {\em abstract ladder operators}, in the attempt to find its eigenvalues and eigenvectors.…

Mathematical Physics · Physics 2026-05-05 Fabio Bagarello , Emanuele Balistreri , Antonino Faddetta

Machine learning of kinetic energy functionals (KEF), in particular kinetic energy density (KED) functionals, has recently attracted attention as a promising way to construct KEFs for orbital-free density functional theory (OF-DFT). Neural…

Materials Science · Physics 2025-08-11 Sergei Manzhos , Johann Lüder , Manabu Ihara