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Statistical Mechanics of Dynamical System Identification
Recovering dynamical equations from observed noisy data is the central challenge of system identification.
Dimensionally Consistent Learning with Buckingham Pi
In the absence of governing equations, dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems.
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders
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.
Data-Driven Discovery of Coarse-Grained Equations
Statistical (machine learning) tools for equation discovery require large amounts of data that are typically computer generated rather than experimentally observed.
Machine Learning for a Music Glove Instrument
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.
