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In this study, we address the limitations of previous solutions for modeling high-order harmonics in semiconductor superlattices (SSLs). Earlier research proposed a step function ansatz that effectively modeled high-order even and odd…

Mesoscale and Nanoscale Physics · Physics 2023-10-10 Abdullah Al-Ateqi , Mauro Fernandes Pereira

We explore the use of first and second order same-time atomic spatial correlation functions as a diagnostic for probing the small scale spatial structure of atomic samples trapped in optical lattices. Assuming an ensemble of equivalent…

Quantum Physics · Physics 2015-06-26 John P. Grondalski , Paul M. Alsing , Ivan H. Deutsch

The zero temperature phase diagram of binary boson-fermion mixtures in two-colour superlattices is investigated. The eigenvalue problem associated with the Bose-Fermi-Hubbard Hamiltonian is solved using an exact numerical diagonalization…

Statistical Mechanics · Physics 2007-05-23 F. Schmitt , M. Hild , R. Roth

In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations. Surrogate modeling of these computer…

Machine Learning · Statistics 2021-11-10 Nathan Wycoff , Mickaël Binois , Robert B. Gramacy

A non-local dynamic homogenization technique for the analysis of a viscoelastic heterogeneous material which displays a periodic microstructure is herein proposed. The asymptotic expansion of the micro-displacement field in the transformed…

Applied Physics · Physics 2018-11-26 Rosaria Del Toro , Andrea Bacigalupo , Marco Paggi

We present a flexible trust region descend algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive problems, i.e., problems that have at least one objective…

Optimization and Control · Mathematics 2021-05-27 Manuel Berkemeier , Sebastian Peitz

Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well established method dealing with the structural behavior of periodic…

Computational Engineering, Finance, and Science · Computer Science 2017-07-17 Charilaos Mylonas , Valentin Bemetz , Eleni Chatzi

Predicting observables in equilibrium states is a central yet notoriously hard question in quantum many-body systems. In the physically relevant thermodynamic limit, certain mathematical formulations of this task have even been shown to…

Quantum Physics · Physics 2025-08-15 Hamza Fawzi , Omar Fawzi , Samuel O. Scalet

Empirical risk minimization (ERM) with a computationally feasible surrogate loss is a widely accepted approach for classification. Notably, the convexity and calibration (CC) properties of a loss function ensure consistency of ERM in…

Machine Learning · Statistics 2024-09-05 Ben Dai

We develop a real-space extension of the dual fermion approach. This method is formulated in terms of real-space Green's functions and local vertex functions, which enables us to discuss local and nonlocal correlations in inhomogeneous…

Strongly Correlated Electrons · Physics 2018-01-09 Nayuta Takemori , Akihisa Koga , Hartmut Hafermann

A lattice of elastic Rayleigh rods organized in a parallelepiped geometry can be axially loaded up to an arbitrary amount without distortion and then be subject to incremental time-harmonic dynamic motion. At certain threshold levels of…

Classical Physics · Physics 2021-03-04 Giovanni Bordiga , Luigi Cabras , Andrea Piccolroaz , Davide Bigoni

We present numerically exact non-equilibrium dynamics of a one-dimensional Bose gas in quasi-periodic lattice that plays an intermediate role between the long-ranged order and truly disordered systems exhibiting unusual correlated phases.…

Quantum Gases · Physics 2025-09-25 Attis V. M. Marino , M. A. Caracanhas , V. S. Bagnato , B. Chakrabarti

Two discrete N-level alternatives to the popular imaginary cubic oscillator are proposed and studied. In a certain domain ${\cal D}$ of parameters $a$ and $z$ of the model, the spectrum of energies is shown real (i.e., potentially,…

Mathematical Physics · Physics 2012-02-06 Miloslav Znojil

The high computational cost of phase field simulations remains a major limitation for predicting dendritic solidification in metals, particularly in additive manufacturing, where microstructural control is critical. This work presents a…

We propose a novel general approach to locality of lattice composite fields, which in case of QCD involves locality in both quark and gauge degrees of freedom. The method is applied to gauge operators based on the overlap Dirac matrix…

High Energy Physics - Lattice · Physics 2017-02-08 Andrei Alexandru , Ivan Horváth

A stress equilibration procedure for linear elasticity is proposed and analyzed in this paper with emphasis on the behavior for (nearly) incompressible materials. Based on the displacement-pressure approximation computed with a stable…

Numerical Analysis · Mathematics 2019-11-01 Fleurianne Bertrand , Bernhard Kober , Marcel Moldenhauer , Gerhard Starke

Understanding structure-property relations is essential to optimally design materials for specific applications. Two-scale simulations are often employed to analyze the effect of the microstructure on a component's macroscopic properties.…

Computational Engineering, Finance, and Science · Computer Science 2022-10-25 Theron Guo , Francesco A. B. Silva , Ondřej Rokoš , Karen Veroy

In this paper I intend to show that macroscopic entanglement is possible at high temperatures. I analyze multipartite entanglement produced by the $\eta$ pairing mechanism which features strongly in the fermionic lattice models of high…

Quantum Physics · Physics 2009-11-10 Vlatko Vedral

We report multipronged progress on the stochastic averaging approach to numerical analytic continuation of quantum Monte Carlo data. With the sampled spectrum parametrized with delta-functions in continuous frequency space, a calculation of…

Strongly Correlated Electrons · Physics 2023-01-11 Hui Shao , Anders W. Sandvik

We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a…

Machine Learning · Statistics 2019-01-16 Yibo Yang , Paris Perdikaris
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