12 citations · 17 across the 4 of their papers we have counts for
5 papers
Dimensionally Consistent Learning with Buckingham Pi
Joseph Bakarji, Jared Callaham, Steven L. Brunton +1
In the absence of governing equations, dimensional analysis is a robust technique for extracting insights and finding symmetries in physical systems. Given measurement variables an…
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…
Stochastic Pore Collapse Models in Granular Materials
Joseph Bakarji, Daniel M. Tartakovsky
Stochastic models for pore collapse in granular materials are developed. First, a general fluctuating stress-strain relation for a plastic flow rule is derived. The fluctuations ac…
Data-Driven Discovery of Coarse-Grained Equations
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. Multiscale mode…
Machine Learning for a Music Glove Instrument
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. Once t…