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Found 5 papers, 0 papers with code
Extended Koopman Models
We introduce two novel generalizations of the Koopman operator method of nonlinear dynamic modeling.
Coarse-Grained Nonlinear System Identification
We also demonstrate this approach experimentally, showing that it identifies an accurate model of the nonlinear voltage to luminosity dynamics of a tungsten filament with less than a second of experimental data.
Rapid Generation of Stochastic Signals with Specified Statistics
We demonstrate a novel algorithm for generating stationary stochastic signals with a specified power spectral density (or equivalently, via the Wiener-Khinchin relation, a specified autocorrelation function) while satisfying constraints on the signal's probability density function.
Neural Group Actions
We introduce an algorithm for designing Neural Group Actions, collections of deep neural network architectures which model symmetric transformations satisfying the laws of a given finite group.
Deep Involutive Generative Models for Neural MCMC
We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC.
