Search Results for author:
Found 7 papers, 3 papers with code
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 analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements.
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
We derive symmetric and antisymmetric kernels by symmetrizing and antisymmetrizing conventional kernels and analyze their properties.
Kernel-based approximation of the Koopman generator and Schrödinger operator
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.
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 generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition).
Tensor-based computation of metastable and coherent sets
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches.
Sparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets.
Ranked #1 on
Denoising
on Darmstadt Noise Dataset
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters.
