195 citations · 652 across the 13 of their papers we have counts for
5 papers · 1 filter
PySINDy: A comprehensive Python package for robust sparse system identification
Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel +9
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySIN…
On the role of nonlinear correlations in reduced-order modeling
Jared L. Callaham, Steven L. Brunton, Jean-Christophe Loiseau
A major goal for reduced-order models of unsteady fluid flows is to uncover and exploit latent low-dimensional structure. Proper orthogonal decomposition (POD) provides an energy-o…
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…