---
res:
  bibo_abstract:
  - <jats:p>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 Schrödinger
    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 Schrödinger 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.</jats:p>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Klus, Stefan
      foaf_surname: Klus
  - foaf_Person:
      foaf_givenName: Feliks
      foaf_name: Nüske, Feliks
      foaf_surname: Nüske
      foaf_workInfoHomepage: http://www.librecat.org/personId=81513
    orcid: 0000-0003-2444-7889
  - foaf_Person:
      foaf_givenName: Boumediene
      foaf_name: Hamzi, Boumediene
      foaf_surname: Hamzi
  bibo_doi: 10.3390/e22070722
  dct_date: 2020^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1099-4300
  dct_language: eng
  dct_title: Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator@
...