Functional dynamic mode decomposition: Learning infinite-dimensional systems from data

arXiv:2609.29159 · math.DS, cs.LG, stat.ML · Submitted 2026-09-24 · Read on arXiv

math.DS, cs.LG, stat.ML

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/sklus/d3s

Terminology

Hilbert Space — 10× in this paper · explained in 2 episodes
Clustering — 6× in this paper · explained in 2 episodes
Metastability — 6× in this paper · explained in 2 episodes
Langevin Dynamics — 5× in this paper · explained in 2 episodes
Spectral clustering — 4× in this paper · explained in 2 episodes
Benchmark — 3× in this paper · explained in 2 episodes
System Identification — 3× in this paper · explained in 3 episodes
Accuracy — 2× in this paper · explained in 1 episode
Generalization — 2× in this paper · explained in 13 episodes
Spectral Gap — 2× in this paper · explained in 5 episodes
Adjacency Matrix — 1× in this paper · explained in 1 episode
Chebyshev Polynomials — 1× in this paper · explained in 2 episodes
Conference on Decision and Control — 1× in this paper
Conservation Laws — 1× in this paper · explained in 3 episodes
Curse of Dimensionality — 1× in this paper · explained in 6 episodes
Diffusion Processes — 1× in this paper · explained in 2 episodes
Dynamic Mode Decomposition (DMD) — 1× in this paper · explained in 3 episodes
Feature Extraction — 1× in this paper · explained in 2 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Inverse Problem — 1× in this paper · explained in 2 episodes
Quantum Physics — 1× in this paper · explained in 10 episodes
Regularization — 1× in this paper · explained in 3 episodes
Vector Representations — 1× in this paper · explained in 2 episodes
Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

Numerical approximation of the Koopman-von Neumann equation: Operator learning and quantum computing

Terminology

Sources

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