5 papers
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…
On Koopman Mode Decomposition and Tensor Component Analysis
William T. Redman
Koopman mode decomposition and tensor component analysis (also known as CANDECOMP/PARAFAC or canonical polyadic decomposition) are two popular approaches of decomposing high dimens…
Local error quantification for Neural Network Differential Equation solvers
Akshunna S. Dogra, William T Redman
Neural networks have been identified as powerful tools for the study of complex systems. A noteworthy example is the neural network differential equation (NN DE) solver, which can…
Renormalization Group as a Koopman Operator
William T Redman
Koopman operator theory is shown to be directly related to the renormalization group. This observation allows us, with no assumption of translational invariance, to compute the cri…
An O(n) method of calculating Kendall correlations of spike trains
William T Redman
The ability to record from increasingly large numbers of neurons, and the increasing attention being paid to large scale neural network simulations, demands computationally fast al…