195 citations · 652 across the 13 of their papers we have counts for
13 papers
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
Multiscale model reduction for incompressible flows
Jared L. Callaham, Jean-Christophe Loiseau, Steven L. Brunton
Many unsteady flows exhibiting complex dynamics are nevertheless characterized by emergent large-scale coherence in space and time. Reduced-order models based on Galerkin projectio…
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…
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…
Nonlinear stochastic modeling with Langevin regression
Jared L. Callaham, Jean-Christophe Loiseau, Georgios Rigas +1
Many physical systems characterized by nonlinear multiscale interactions can be effectively modeled by treating unresolved degrees of freedom as random fluctuations. However, even…
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…
Robust flow field reconstruction from limited measurements via sparse representation
Jared Callaham, Kazuki Maeda, Steven L. Brunton
In many applications it is important to estimate a fluid flow field from limited and possibly corrupt measurements. Current methods in flow estimation often use least squares regre…
Population Annealing Simulations of a Binary Hard Sphere Mixture
Jared Callaham, Jon Machta
Population annealing is a sequential Monte Carlo scheme well-suited to simulating equilibrium states of systems with rough free energy landscapes. Here we use population annealing…