Data-driven approximation methods are derived for the unitary Koopman-von Neumann operator, its eigenvalues and eigenfunctions, with explicit quantum-circuit representations for finite-dimensional projections.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
A data-driven framework reduces particle-based transfer operators via concentration projection, geometric manifold, and finite-state discretization to reproduce clustering transitions and metastable states from simulation data.
Optimizing the activation function in randomized neural networks provides a more suitable dictionary for transfer operator approximation in stochastic differential equations and random walks on graphons.
citing papers explorer
-
Numerical approximation of the Koopman-von Neumann equation: Operator learning and quantum computing
Data-driven approximation methods are derived for the unitary Koopman-von Neumann operator, its eigenvalues and eigenfunctions, with explicit quantum-circuit representations for finite-dimensional projections.
-
Data-driven Reduction of Transfer Operators for Particle Clustering Dynamics
A data-driven framework reduces particle-based transfer operators via concentration projection, geometric manifold, and finite-state discretization to reproduce clustering transitions and metastable states from simulation data.
-
Optimization of randomized neural networks for transfer operator approximation
Optimizing the activation function in randomized neural networks provides a more suitable dictionary for transfer operator approximation in stochastic differential equations and random walks on graphons.
- Kolmogorov equations for evaluating the boundary hitting of degenerate diffusion with unsteady drift