47 citations · 48 across the 5 of their papers we have counts for
5 papers
Forecasting Local Ionospheric Parameters Using Transformers
Daniel J. Alford-Lago, Christopher W. Curtis, Alexander T. Ihler +2
We present a novel method for forecasting key ionospheric parameters using transformer-based neural networks. The model provides accurate forecasts and uncertainty quantification o…
Entropic Regression DMD (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models
Christopher W. Curtis, Erik Bollt, Daniel Jay Alford-Lago
In this work, we present a method which determines optimal multi-step dynamic mode decomposition (DMD) models via entropic regression, which is a nonlinear information flow detecti…
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…
Scale-Separated Dynamic Mode Decomposition and Ionospheric Forecasting
Daniel J. Alford-Lago, Christopher W. Curtis, Alexander T. Ihler +1
We present a method for forecasting the foF2 and hmF2 parameters using modal decompositions of ionospheric electron density profile time series. Our method is based on the Dynamic…
Deep Learning Enhanced Dynamic Mode Decomposition
Daniel J. Alford-Lago, Christopher W. Curtis, Alexander T. Ihler +1
Koopman operator theory shows how nonlinear dynamical systems can be represented as an infinite-dimensional, linear operator acting on a Hilbert space of observables of the system.…