577 citations · 3.2k across the 74 of their papers we have counts for
76 papers
The problem of infinite information flow
Zheng Bian, Erik M. Bollt
We study conditional mutual information (cMI) between a pair of variables given a third one and derived quantities including transfer entropy (TE) and causation entropy (…
Kolmogorov-Arnold Network Autoencoders
Mohammadamin Moradi, Shirin Panahi, Erik Bollt +1
Deep learning models have revolutionized various domains, with Multi-Layer Perceptrons (MLPs) being a cornerstone for tasks like data regression and image classification. However,…
Data-driven model discovery with Kolmogorov-Arnold networks
Mohammadamin Moradi, Shirin Panahi, Erik M. Bollt +1
Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing…
Tree-based Learning for High-Fidelity Prediction of Chaos
Adam Giammarese, Kamal Rana, Erik M. Bollt +1
Model-free forecasting of the temporal evolution of chaotic systems is crucial but challenging. Existing solutions require hyperparameter tuning, significantly hindering their wide…
Autoencoding for the 'Good Dictionary' of eigen pairs of the Koopman Operator
Neranjaka Jayarathne, Erik M. Bollt
Reduced order modelling relies on representing complex dynamical systems using simplified modes, which can be achieved through Koopman operator analysis. However, computing Koopman…
Machine Learning Enhanced Hankel Dynamic-Mode Decomposition
Christopher W. Curtis, D. Jay Alford-Lago, Erik Bollt +1
While the acquisition of time series has become more straightforward, developing dynamical models from time series is still a challenging and evolving problem domain. Within the la…