34 citations · 55 across the 5 of their papers we have counts for
7 papers
Trustworthy Koopman Operator Learning: Invariance Diagnostics and Error Bounds
Gustav Conradie, Nicolas Boullé, Jean-Christophe Loiseau +2
Koopman operator theory provides a global linear representation of nonlinear dynamics and underpins many data-driven methods. In practice, however, finite-dimensional feature space…
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…
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.…
PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data
Brian M. de Silva, Kathleen Champion, Markus Quade +3
PySINDy is a Python package for the discovery of governing dynamical systems models from data. In particular, PySINDy provides tools for applying the sparse identification of nonli…
Data-driven modeling of the chaotic thermal convection in an annular thermosyphon
Jean-Christophe Loiseau
dentifying accurate and yet interpretable low-order models from data has gained a renewed interest over the past decade. In the present work, we illustrate how the combined use of…
Time-stepping and Krylov methods for large-scale instability problems
Jean-Christophe Loiseau, Michele Alessandro Bucci, Stefania Cherubini +1
With the ever increasing computational power available and the development of high-performances computing, investigating the properties of realistic very large-scale nonlinear dyna…