1 citations · 1 across the 1 of their papers we have counts for
3 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…