5 papers
Higher-Order Kolmogorov-Arnold Networks for Dynamics
Kevin Slote, Jeremie Fish
The ability to derive the governing equations of a dynamical system from data is essential for prediction and control across many scientific fields. Two challenges, however, are th…
Deep-Koopman-KANDy: Dictionary Discovery for Deep-Koopman Operators with Kolmogorov-Arnold Networks for Dynamics
Kevin Slote, Erik Bollt, Jeremie Fish
Symbolic library -- or Koopman dictionary -- selection is a fundamental challenge in data-driven dynamical systems. Extended Dynamic Mode Decomposition (EDMD), Sparse Identificatio…
KANDy: Kolmogorov-Arnold Networks and Dynamical System Discovery
Kevin Slote, Jeremie Fish, Erik Bollt
We introduce the Kolmogorov-Arnold Network for Dynamics (KANDy) as a zero-depth, wide neural architecture capable of discovering governing equations in chaotic and complex dynamica…
CausationEntropy: Pythonic Optimal Causation Entropy
Kevin Slote, Jeremie Fish, Erik Bollt
Optimal Causation Entropy (oCSE) is a robust causal network modeling technique that reveals causal networks from dynamical systems and coupled oscillators, distinguishing direct fr…
Online Performance Estimation with Unlabeled Data: A Bayesian Application of the Hui-Walter Paradigm
Kevin Slote, Elaine Lee
In the industrial practice of machine learning and statistical modeling, practitioners often work under the assumption of accessible, static, labeled data for evaluation and traini…