no code implementations • 4 Mar 2024 • Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar
Recovering dynamical equations from observed noisy data is the central challenge of system identification.
no code implementations • 9 Feb 2022 • Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
In the absence of governing equations, dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems.
no code implementations • 13 Jan 2022 • Joseph Bakarji, Kathleen Champion, J. Nathan Kutz, Steven L. Brunton
Here, we design a custom deep autoencoder network to learn a coordinate transformation from the delay embedded space into a new space where it is possible to represent the dynamics in a sparse, closed form.
no code implementations • 30 Jan 2020 • Joseph Bakarji, Daniel M. Tartakovsky
Statistical (machine learning) tools for equation discovery require large amounts of data that are typically computer generated rather than experimentally observed.
no code implementations • 27 Jan 2020 • Joseph Bakarji
A music glove instrument equipped with force sensitive, flex and IMU sensors is trained on an electric piano to learn note sequences based on a time series of sensor inputs.