Structure-Preserving Dynamic Mode Decomposition for Highly Oscillatory Dynamics of Semiclassical Schrödinger Equations
This work addresses the challenge of efficiently modeling quantum mechanical systems for researchers in computational physics and applied mathematics, though it appears incremental as it builds on existing DMD-type methods with specific enhancements.
The authors tackled the problem of predicting highly oscillatory dynamics in semiclassical Schrödinger equations by proposing two novel data-driven dynamic mode decomposition methods that incorporate mass and energy conservation laws, resulting in methods that are noise-robust, computationally efficient, and transferable to other equations.
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 Schrödinger 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 Schrödinger 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.