21 citations · 54 across the 17 of their papers we have counts for
7 papers · 1 filter
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
Mars Liyao Gao, Jan P. Williams, J. Nathan Kutz
Modeling real-world spatio-temporal data is exceptionally difficult due to inherent high dimensionality, measurement noise, partial observations, and often expensive data collectio…
Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems
Doris Voina, Steven Brunton, J. Nathan Kutz
A significant challenge in many fields of science and engineering is making sense of time-dependent measurement data by recovering governing equations in the form of differential e…
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
L. Mars Gao, J. Nathan Kutz
Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under constraints allows joint discoveries of governing equations and latent coord…
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data
Alex Mallen, Christoph A. Keller, J. Nathan Kutz
In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant action…
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
Alex Mallen, Henning Lange, J. Nathan Kutz
Probabilistic forecasting of complex phenomena is paramount to various scientific disciplines and applications. Despite the generality and importance of the problem, general mathem…
Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
Charles B. Delahunt, Courosh Mehanian, J. Nathan Kutz
Softmax is a standard final layer used in Neural Nets (NNs) to summarize information encoded in the trained NN and return a prediction. However, Softmax leverages only a subset of…