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The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving
  Dynamical Systems

The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems

6 September 2022
Matthew J. Colbrook
ArXivPDFHTML

Papers citing "The mpEDMD Algorithm for Data-Driven Computations of Measure-Preserving Dynamical Systems"

13 / 13 papers shown
Title
Limits and Powers of Koopman Learning
Limits and Powers of Koopman Learning
Matthew J. Colbrook
Igor Mezić
Alexei Stepanenko
27
10
0
08 Jul 2024
Multiplicative Dynamic Mode Decomposition
Multiplicative Dynamic Mode Decomposition
Nicolas Boullé
Matthew J. Colbrook
21
2
0
08 May 2024
Rigged Dynamic Mode Decomposition: Data-Driven Generalized Eigenfunction
  Decompositions for Koopman Operators
Rigged Dynamic Mode Decomposition: Data-Driven Generalized Eigenfunction Decompositions for Koopman Operators
Matthew J. Colbrook
Catherine Drysdale
Andrew Horning
24
5
0
01 May 2024
Koopman-Assisted Reinforcement Learning
Koopman-Assisted Reinforcement Learning
Preston Rozwood
Edward Mehrez
Ludger Paehler
Wen Sun
Steven L. Brunton
21
6
0
04 Mar 2024
PyDMD: A Python package for robust dynamic mode decomposition
PyDMD: A Python package for robust dynamic mode decomposition
Sara M. Ichinaga
Francesco Andreuzzi
N. Demo
M. Tezzele
Karl Lapo
G. Rozza
Steven L. Brunton
J. Nathan Kutz
AI4CE
28
16
0
12 Feb 2024
On the Convergence of Hermitian Dynamic Mode Decomposition
On the Convergence of Hermitian Dynamic Mode Decomposition
Nicolas Boullé
Matthew J. Colbrook
19
2
0
06 Jan 2024
Information theory for data-driven model reduction in physics and
  biology
Information theory for data-driven model reduction in physics and biology
Matthew S. Schmitt
Maciej Koch-Janusz
Michel Fruchart
Daniel S. Seara
Michael Rust
Vincenzo Vitelli
13
4
0
11 Dec 2023
Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with
  Automatic Differentiation: Koopman and Neural ODE Approaches
Enhancing Predictive Capabilities in Data-Driven Dynamical Modeling with Automatic Differentiation: Koopman and Neural ODE Approaches
Ricardo Constante-Amores
Alec J. Linot
Michael D. Graham
11
10
0
10 Oct 2023
Beyond expectations: Residual Dynamic Mode Decomposition and Variance
  for Stochastic Dynamical Systems
Beyond expectations: Residual Dynamic Mode Decomposition and Variance for Stochastic Dynamical Systems
Matthew J. Colbrook
Qin Li
Ryan V. Raut
Alex Townsend
19
16
0
21 Aug 2023
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman
  Operator
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator
Shaowu Pan
E. Kaiser
Brian M. de Silva
J. Nathan Kutz
Steven L. Brunton
11
8
0
22 Jun 2023
Orthogonal polynomial approximation and Extended Dynamic Mode
  Decomposition in chaos
Orthogonal polynomial approximation and Extended Dynamic Mode Decomposition in chaos
Caroline L. Wormell
10
3
0
14 May 2023
Learning Bilinear Models of Actuated Koopman Generators from
  Partially-Observed Trajectories
Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories
Samuel E. Otto
Sebastian Peitz
C. Rowley
23
19
0
20 Sep 2022
Residual Dynamic Mode Decomposition: Robust and verified Koopmanism
Residual Dynamic Mode Decomposition: Robust and verified Koopmanism
Matthew J. Colbrook
Lorna J. Ayton
Máté Szőke
21
58
0
19 May 2022
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