research
∙
07/28/2023
Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems
The Koopman operator has become an essential tool for data-driven analys...
share
research
∙
03/31/2021
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
We derive symmetric and antisymmetric kernels by symmetrizing and antisy...
research
∙
05/27/2020
Kernel-based approximation of the Koopman generator and Schrödinger operator
Many dimensionality and model reduction techniques rely on estimating do...
research
∙
09/23/2019
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
We derive a data-driven method for the approximation of the Koopman gene...
research
∙
08/12/2019
Tensor-based EDMD for the Koopman analysis of high-dimensional systems
Recent years have seen rapid advances in the data-driven analysis of dyn...
research
∙
12/06/2017
Sparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is ...
research
∙
10/20/2016