Structure-Preserving Dynamic Mode Decomposition for Highly Oscillatory Dynamics of Semiclassical Schr\"odinger Equations

TL;DR

This paper introduces two novel DMD-type methods, Crank--Nicolson DMD and semi-implicit DMD, for efficient and accurate prediction of highly oscillatory semiclassical Schrödinger dynamics, ensuring physical fidelity and model reduction.

Contribution

The paper develops structure-preserving DMD methods that explicitly incorporate conservation laws, providing theoretical guarantees and improved efficiency over existing approaches.

Findings

Methods accurately predict oscillatory dynamics

Demonstrated robustness to noise and transferability

Achieved computational efficiency and physical fidelity

Abstract

We propose two novel data-driven dynamic mode decomposition (DMD)-type methods, the Crank--Nicolson DMD and the semi-implicit DMD, to predict the highly oscillatory dynamics of the semiclassical Schr\"odinger equations efficiently and accurately. Unlike many existing DMD-type methods which directly models the dynamics of the wave function, our approach is based on learning the Schr\"odinger operator while explicitly incorporating mass and energy conservation laws. This approach ensures physical fidelity and endows the resulting methods with built-in model order reduction capabilities, without the necessity for additional dimensionality-reduction preprocessing. An analysis of training and prediction errors are given for theoretical guarantees. Extensive numerical experiments demonstrate the noise robustness, computational efficiency, and transferability to other equations of the proposed methods.