Shadow Lagrangian dynamics for superfluidity

TL;DR

This paper introduces a novel shadow Lagrangian framework for superfluid quantum states, enabling efficient linear discretization and error estimation through an extended harmonic oscillator approach.

Contribution

It proposes an extended shadow Lagrangian density for superfluid quantum states, coupling a wave function with an auxiliary field for improved numerical simulation.

Findings

Efficient linear time-stepping discretization methods are developed.

A consistency error indicator is derived for adaptive error control.

Numerical experiments validate the proposed approach.

Abstract

Motivated by a similar approach for Born-Oppenheimer molecular dynamics, this paper proposes an extended "shadow" Lagrangian density for quantum states of superfluids. The extended Lagrangian contains an additional field variable that is forced to follow the wave function of the quantum state through a rapidly oscillating extended harmonic oscillator. By considering the adiabatic limit for large frequencies of the harmonic oscillator, we can derive the two equations of motions, a Schr\"odinger-type equation for the quantum state and a wave equation for the extended field variable. The equations are coupled in a nonlinear way, but each equation individually is linear with respect to the variable that it defines. The computational advantage of this new system is that it can be easily discretized using linear time stepping methods, where we propose to use a Crank-Nicolson-type approach for the Schr\"odinger equation and an extended leapfrog scheme for the wave equation. Furthermore, the difference between the quantum state and the extended field variable defines a consistency error that should go to zero if the frequency tends to infinity. By coupling the time-step size in our discretization to the frequency of the harmonic oscillator we can extract an easily computable consistency error indicator that can be used to estimate the numerical error without any additional costs. The findings are illustrated in numerical experiments.