35 citations · 59 across the 3 of their papers we have counts for
3 papers
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders
Joseph Bakarji, Kathleen Champion, J. Nathan Kutz +1
A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens' theorem pro…
Structured Time-Delay Models for Dynamical Systems with Connections to Frenet-Serret Frame
Seth M. Hirsh, Sara M. Ichinaga, Steven L. Brunton +2
Time-delay embeddings and dimensionality reduction are powerful techniques for discovering effective coordinate systems to represent the dynamics of physical systems. Recently, it…
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data
Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
The sparse identification of nonlinear dynamics (SINDy) is a regression framework for the discovery of parsimonious dynamic models and governing equations from time-series data. As…