1 citations · 1 across the 5 of their papers we have counts for
5 papers
Optimization of randomized neural networks for transfer operator approximation
Mohammad Tabish, Stefan Klus
RaNNDy is a randomized neural network architecture for the data-driven approximation of transfer operators associated with complex dynamical systems. The weights and biases of the…
Numerical approximation of the Koopman-von Neumann equation: Operator learning and quantum computing
Stefan Klus, Feliks Nüske, Patrick Gelß
The Koopman-von Neumann equation describes the evolution of wavefunctions associated with autonomous ordinary differential equations and can be regarded as a quantum physics-inspir…
How deep is your network? Deep vs. shallow learning of transfer operators
Mohammad Tabish, Benedict Leimkuhler, Stefan Klus
We propose a randomized neural network approach called RaNNDy for learning transfer operators and their spectral decompositions from data. The weights of the hidden layers of the n…
Learning graphons from data: Random walks, transfer operators, and spectral clustering
Stefan Klus, Jason J. Bramburger
Many signals evolve in time as a stochastic process, randomly switching between states over discretely sampled time points. Here we make an explicit link between the underlying sto…
Learning dynamical systems from data: Gradient-based dictionary optimization
Mohammad Tabish, Neil K. Chada, Stefan Klus
The Koopman operator plays a crucial role in analyzing the global behavior of dynamical systems. Existing data-driven methods for approximating the Koopman operator or discovering…