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In the present work, a new time-dependent exchange theory is presented wherein the symmetry constraints, on a multi-electron wavefunction, are properly accounted for. In so doing, the equations of motion, incorporating the required…

Computational Physics · Physics 2007-05-23 Charles A. Weatherford

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by…

Recent advancements of intermediate-scale quantum processors have triggered tremendous interest in the exploration of practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital…

We present an acceleration method for sequences of large-scale linear systems, such as the ones arising from the numerical solution of time-dependent partial differential equations coupled with algebraic constraints. We discuss different…

Numerical Analysis · Mathematics 2024-03-29 Margherita Guido , Daniel Kressner , Paolo Ricci

In this paper, we design, analyze, and numerically validate positive and energy-dissipating schemes for solving the time-dependent multi-dimensional system of Poisson-Nernst-Planck (PNP) equations, which has found much use in the modeling…

Numerical Analysis · Mathematics 2020-02-24 Hailiang Liu , Wumaier Maimaitiyiming

Many astrophysical simulations involve extreme dynamic range of timescales around 'special points' in the domain (e.g. black holes, stars, planets, disks, galaxies, shocks, mixing interfaces), where processes on small scales couple strongly…

Instrumentation and Methods for Astrophysics · Physics 2026-05-11 Philip F. Hopkins , Elias R. Most

We present evidence that tunneling processes in near-integrable systems are enhanced due to the manifestation of nonlinear resonances and their respective island chains in phase space. A semiclassical description of this…

Chaotic Dynamics · Physics 2015-06-26 Olivier Brodier , Peter Schlagheck , Denis Ullmo

For autonomous systems it is well known how to extract tunneling probabilities from wavepacket calculations. Here we present a corresponding approach for periodically time-dependent Hamiltonians, valid at all frequencies, field strengths,…

Mesoscale and Nanoscale Physics · Physics 2009-11-10 Frank Grossmann

We present an efficient perturbative method to obtain both static and dynamic polarizabilities and hyperpolarizabilities of complex electronic systems. This approach is based on the solution of a frequency dependent Sternheimer equation,…

Materials Science · Physics 2009-11-13 Xavier Andrade , Silvana Botti , Miguel A. L. Marques , Angel Rubio

The importance of simulating pinning arrays in superconducting samples for the increase of critical currents has been increasing in the last few years. Since the Time Dependent Ginzburg Landau (TDGL) can be more accurate than alternative…

Superconductivity · Physics 2026-04-06 E. R. Di Lascio

Multiple time scale molecular dynamics enhances computational efficiency by updating slow motions less frequently than fast motions. However, in practice the largest outer time step possible is limited not by the physical forces but by…

Statistical Mechanics · Physics 2011-01-13 Joseph A. Morrone , Thomas E. Markland , Michele Ceriotti , B. J. Berne

A vertical resonant tunneling diode (RTD) based on the paramagnetic Zn1-x-yMnyCdxSe system has been fabricated with a pillar diameter down to ~ 6 micron. The diode exhibits high quality resonant tunneling characteristics through the…

Mesoscale and Nanoscale Physics · Physics 2009-11-13 Z. L. Fang , P. Wu , N. Kundtz , A. M. Chang , X. Y. Liu , J. K. Furdyna

An algorithm for a family of self-starting high-order implicit time integration schemes with controllable numerical dissipation is proposed for both linear and nonlinear transient problems. This work builds on the previous works of the…

Numerical Analysis · Mathematics 2024-09-23 Daniel O'Shea , Xiaoran Zhang , Shayan Mohammadian , Chongmin Song

Applications of neural networks to condensed matter physics are becoming popular and beginning to be well accepted. Obtaining and representing the ground and excited state wave functions are examples of such applications. Another…

Disordered Systems and Neural Networks · Physics 2019-12-30 Tomi Ohtsuki , Tomohiro Mano

In this article we develop algorithms for data assimilation based upon a computational time dependent stable/unstable splitting. Our particular method is based upon shadowing refinement and synchronization techniques and is motivated by…

Dynamical Systems · Mathematics 2017-07-31 Bart de Leeuw , Svetlana Dubinkina , Jason Frank , Andrew Steyer , Xuemin Tu , Erik Van Vleck

We present an explicit multiscale algorithm for solving differential equations for problems with high-frequency modes that can be averaged over by separating and scaling the fast and slow dynamics within a single equation. We introduce a…

Plasma Physics · Physics 2026-05-29 Maxwell H. Rosen , Manaure Francisquez , Gregory W. Hammett

We present a microscopic derivation of a master equation for two-component bosons (bosonic qubits) which tunnel between spatially separated modes under local dephasing noise. Starting from the full system-bath Hamiltonian with Lorentzian…

Quantum Physics · Physics 2026-03-10 Alberto Ferrara , Farzam Nosrati , Andrea Smirne , Jyrki Piilo , Rosario Lo Franco

We provide a general method for efficiently simulating time-dependent Hamiltonian dynamics on a circuit-model based quantum computer. Our approach is based on approximating the truncated Dyson series of the evolution operator, extending the…

Quantum Physics · Physics 2019-04-10 Maria Kieferova , Artur Scherer , Dominic Berry

We show how to determine (reconstruct) a master equation governing the time evolution of an open quantum system. We present a general algorithm for the reconstruction of the corresponding Liouvillian superoperators. Dynamics of a two-level…

Quantum Physics · Physics 2016-09-08 Vladimir Buzek

In this paper, we show that a revised convolutional recurrent neural network (CRNN) can decrease, by orders of magnitude, the time needed for the phase-resolved prediction of waves in a spatiotemporal domain of a nonlinear dispersive wave…

Fluid Dynamics · Physics 2020-08-04 Fazlolah Mohaghegh , Mohammad-Reza Alam , Jayathi Murthy