Name
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 Schrodinger 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 Schrodinger 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/e22070722 | ENTROPY |
Keywords | DocType | Volume |
Koopman generator,Schrodinger operator,reproducing kernel Hilbert space | Journal | 22 |
Issue | ISSN | Citations |
7 | 1099-4300 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Klus | 1 | 17 | 6.09 |
| Feliks Nüske | 2 | 0 | 0.34 |
| Boumediene Hamzi | 3 | 40 | 10.57 |