19 citations · 22 across the 3 of their papers we have counts for
7 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…
An empirical mean-field model of symmetry-breaking in a turbulent wake
Jared L. Callaham, Georgios Rigas, Jean-Christophe Loiseau +1
This work develops a low-dimensional nonlinear stochastic model of symmetry-breaking coherent structures from experimental measurements of a turbulent axisymmetric bluff body wake.…
Promoting global stability in data-driven models of quadratic nonlinear dynamics
Alan A. Kaptanoglu, Jared L. Callaham, Christopher J. Hansen +2
Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of reduced-order models for a variety of scientific and engineering tasks. However, it is…
Data-driven stochastic modeling of coarse-grained dynamics with finite-size effects using Langevin regression
Jordan Snyder, Jared L. Callaham, Steven L. Brunton +1
Obtaining coarse-grained models that accurately incorporate finite-size effects is an important open challenge in the study of complex, multi-scale systems. We apply Langevin regre…
Physics-informed machine learning for sensor fault detection with flight test data
Brian M. de Silva, Jared Callaham, Jonathan Jonker +7
We develop data-driven algorithms to fully automate sensor fault detection in systems governed by underlying physics. The proposed machine learning method uses a time series of typ…
Learning dominant physical processes with data-driven balance models
Jared L. Callaham, James V. Koch, Bingni W. Brunton +2
Throughout the history of science, physics-based modeling has relied on judiciously approximating observed dynamics as a balance between a few dominant processes. However, this tra…