Tensor network approximation of Koopman operators

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Giannakis, Dimitrios, Jebelli, Mohammad Javad Latifi, Montgomery, Michael, Pfeffer, Philipp, Schumacher, Jörg, Slawinska, Joanna
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866912790173712384
author Giannakis, Dimitrios
Jebelli, Mohammad Javad Latifi
Montgomery, Michael
Pfeffer, Philipp
Schumacher, Jörg
Slawinska, Joanna
author_facet Giannakis, Dimitrios
Jebelli, Mohammad Javad Latifi
Montgomery, Michael
Pfeffer, Philipp
Schumacher, Jörg
Slawinska, Joanna
contents We propose a tensor network framework for approximating the evolution of observables of measure-preserving ergodic systems. Our approach is based on a spectrally-convergent approximation of the skew-adjoint Koopman generator by a diagonalizable, skew-adjoint operator $W_τ$ that acts on a reproducing kernel Hilbert space $\mathcal H_τ$ with coalgebra structure and Banach algebra structure under the pointwise product of functions. Leveraging this structure, we lift the unitary evolution operators $e^{t W_τ}$ (which can be thought of as regularized Koopman operators) to a unitary evolution group on the Fock space $F(\mathcal H_τ)$ generated by $\mathcal H_τ$ that acts multiplicatively with respect to the tensor product. Our scheme also employs a representation of classical observables ($L^\infty$ functions of the state) by quantum observables (self-adjoint operators) acting on the Fock space, and a representation of probability densities in $L^1$ by quantum states. Combining these constructions leads to an approximation of the Koopman evolution of observables that is representable as evaluation of a tree tensor network built on a tensor product subspace $\mathcal H_τ^{\otimes n} \subset F(\mathcal H_τ)$ of arbitrarily high grading $n \in \mathbb N$. A key feature of this quantum-inspired approximation is that it captures information from a tensor product space of dimension $(2d+1)^n$, generated from a collection of $2d + 1$ eigenfunctions of $W_τ$. Furthermore, the approximation is positivity preserving. The paper contains a theoretical convergence analysis of the method and numerical applications to two dynamical systems on the 2-torus: an ergodic torus rotation as an example with pure point Koopman spectrum and a Stepanoff flow as an example with topological weak mixing.
format Preprint
id arxiv_https___arxiv_org_abs_2407_07242
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Tensor network approximation of Koopman operators
Giannakis, Dimitrios
Jebelli, Mohammad Javad Latifi
Montgomery, Michael
Pfeffer, Philipp
Schumacher, Jörg
Slawinska, Joanna
Dynamical Systems
Quantum Physics
We propose a tensor network framework for approximating the evolution of observables of measure-preserving ergodic systems. Our approach is based on a spectrally-convergent approximation of the skew-adjoint Koopman generator by a diagonalizable, skew-adjoint operator $W_τ$ that acts on a reproducing kernel Hilbert space $\mathcal H_τ$ with coalgebra structure and Banach algebra structure under the pointwise product of functions. Leveraging this structure, we lift the unitary evolution operators $e^{t W_τ}$ (which can be thought of as regularized Koopman operators) to a unitary evolution group on the Fock space $F(\mathcal H_τ)$ generated by $\mathcal H_τ$ that acts multiplicatively with respect to the tensor product. Our scheme also employs a representation of classical observables ($L^\infty$ functions of the state) by quantum observables (self-adjoint operators) acting on the Fock space, and a representation of probability densities in $L^1$ by quantum states. Combining these constructions leads to an approximation of the Koopman evolution of observables that is representable as evaluation of a tree tensor network built on a tensor product subspace $\mathcal H_τ^{\otimes n} \subset F(\mathcal H_τ)$ of arbitrarily high grading $n \in \mathbb N$. A key feature of this quantum-inspired approximation is that it captures information from a tensor product space of dimension $(2d+1)^n$, generated from a collection of $2d + 1$ eigenfunctions of $W_τ$. Furthermore, the approximation is positivity preserving. The paper contains a theoretical convergence analysis of the method and numerical applications to two dynamical systems on the 2-torus: an ergodic torus rotation as an example with pure point Koopman spectrum and a Stepanoff flow as an example with topological weak mixing.
title Tensor network approximation of Koopman operators
topic Dynamical Systems
Quantum Physics
url https://arxiv.org/abs/2407.07242