13 citations · 18 across the 3 of their papers we have counts for
5 papers
Learning Parametric Koopman Decompositions for Prediction and Control
Yue Guo, Milan Korda, Ioannis G. Kevrekidis +1
We present an approach to construct approximate Koopman-type decompositions for dynamical systems depending on static or time-varying parameters. Our method simultaneously construc…
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Danimir T. Doncevic, Alexander Mitsos, Yue Guo +4
Meta-learning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters. To…
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators
Yue Guo, Felix Dietrich, Tom Bertalan +4
We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design of general algorithm structure with a data-driven a…
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions
Erik M. Bollt, Qianxiao Li, Felix Dietrich +1
Matching dynamical systems, through different forms of conjugacies and equivalences, has long been a fundamental concept, and a powerful tool, in the study and classification of no…
An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning
Felix P. Kemeth, Sindre W. Haugland, Felix Dietrich +7
Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, use…