20 citations · 52 across the 13 of their papers we have counts for
13 papers
Koopman-driven grip force prediction through EMG sensing
Tomislav Bazina, Ervin Kamenar, Maria Fonoberova +1
Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities. Robotic rehabilitation provides tools to restore hand function, wh…
Koopman Learning with Episodic Memory
William T. Redman, Dean Huang, Maria Fonoberova +1
Koopman operator theory has found significant success in learning models of complex, real-world dynamical systems, enabling prediction and control. The greater interpretability and…
A Koopman Operator-Based Prediction Algorithm and its Application to COVID-19 Pandemic
Igor Mezic, Zlatko Drmac, Nelida Crnjaric-Zic +6
The problem of prediction of behavior of dynamical systems has undergone a paradigm shift in the second half of the 20th century with the discovery of the possibility of chaotic dy…
Identifying Equivalent Training Dynamics
William T. Redman, Juan M. Bello-Rivas, Maria Fonoberova +3
Study of the nonlinear evolution deep neural network (DNN) parameters undergo during training has uncovered regimes of distinct dynamical behavior. While a detailed understanding o…
Algorithmic (Semi-)Conjugacy via Koopman Operator Theory
William T. Redman, Maria Fonoberova, Ryan Mohr +2
Iterative algorithms are of utmost importance in decision and control. With an ever growing number of algorithms being developed, distributed, and proprietarized, there is a simila…
Koopman Reduced Order Modeling with Confidence Bounds
Ryan Mohr, Maria Fonoberova, Igor Mezic
This paper introduces a reduced order modeling technique based on Koopman operator theory that gives confidence bounds on the model's predictions. It is based on a data-driven spec…
An Operator Theoretic View on Pruning Deep Neural Networks
William T. Redman, Maria Fonoberova, Ryan Mohr +2
The discovery of sparse subnetworks that are able to perform as well as full models has found broad applied and theoretical interest. While many pruning methods have been developed…
Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Ryan Mohr, Maria Fonoberova, Zlatko Drmač +2
Hierarchical support vector regression (HSVR) models a function from data as a linear combination of SVR models at a range of scales, starting at a coarse scale and moving to finer…
Applications of Koopman Mode Analysis to Neural Networks
Iva Manojlović, Maria Fonoberova, Ryan Mohr +4
We consider the training process of a neural network as a dynamical system acting on the high-dimensional weight space. Each epoch is an application of the map induced by the optim…
Exponentially decaying modes and long-term prediction of sea ice concentration using Koopman Mode Decomposition
James Hogg, Maria Fonoberova, Igor Mezic
Sea ice cover in the Arctic and Antarctic is an important indicator of changes in the climate, with important environmental, economic and security consequences. The complexity of t…
Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control
Carl Folkestad, Daniel Pastor, Igor Mezic +3
This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can…
Spectral Complexity of Directed Graphs and Application to Structural Decomposition
Igor Mezić, Vladimir A. Fonoberov, Maria Fonoberova +1
We introduce a new measure of complexity (called spectral complexity) for directed graphs. We start with splitting of the directed graph into its recurrent and non-recurrent parts.…
Quasi-Periodic Intermittency in Oscillating Cylinder Flow
Bryan Glaz, Igor Mezic, Maria Fonoberova +1
Fluid dynamics induced by periodically forced flow around a cylinder is analyzed computationally for the case when the forcing frequency is much lower than the von K{á}rm{á}n vorte…