Kernel-based approximation of the Koopman generator and Schr\"odinger operator

TL;DR

This paper introduces a kernel-based approach to approximate the Koopman generator and Schrödinger operator, enabling eigenfunction estimation for high-dimensional dynamical systems and quantum mechanics applications.

Contribution

It develops a novel kernel-based method for differential operator approximation in RKHS, linking quantum and stochastic systems for improved analysis.

Findings

Effective eigenfunction estimation in molecular dynamics.

Application of methods to quantum chemistry problems.

Transformation between Schrödinger and Kolmogorov operators.

Abstract

Many dimensionality and model reduction techniques rely on estimating dominant eigenfunctions of associated dynamical operators from data. Important examples include the Koopman operator and its generator, but also the Schr\"odinger operator. We propose a kernel-based method for the approximation of differential operators in reproducing kernel Hilbert spaces and show how eigenfunctions can be estimated by solving auxiliary matrix eigenvalue problems. The resulting algorithms are applied to molecular dynamics and quantum chemistry examples. Furthermore, we exploit that, under certain conditions, the Schr\"odinger operator can be transformed into a Kolmogorov backward operator corresponding to a drift-diffusion process and vice versa. This allows us to apply methods developed for the analysis of high-dimensional stochastic differential equations to quantum mechanical systems.