236 citations · 746 across the 18 of their papers we have counts for
18 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…
Krylov methods for large-scale dynamical systems: Application in fluid dynamics
Ricardo S. Frantz, Jean-Christophe Loiseau, Jean-Christophe Robinet
In fluid dynamics, predicting and characterizing bifurcations, from the onset of unsteadiness to the transition to turbulence, is of critical importance for both academic and indus…
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…
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.…
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Omar Chehab, Alexandre Defossez, Jean-Christophe Loiseau +2
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they ca…
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…
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…
Sparse reduced-order modeling : Sensor-based dynamics to full-state estimation
Jean-Christophe Loiseau, Bernd R. Noack, Steven L. Brunton
We propose a general dynamic reduced-order modeling framework for typical experimental data: time-resolved sensor data and optional non-time-resolved PIV snapshots. This framework…
Numerical simulations of elastic capsules with nucleus in shear flow
Arash Alizad Banaei, Jean-Christophe Loiseau, Iman Lashgari +1
The shear-induced deformation of a capsule with a stiff nucleus, a model of eukaryotic cells, is studied numerically. The membrane of the cell and of its nucleus are modelled as a…
Effect of viscosity ratio on the self-sustained instabilities in planar immiscible jets
Outi Tammisola, Jean-Christophe Loiseau, Luca Brandt
Previous studies have shown that intermediate surface tension has a counterintuitive destabilizing effect on 2-phase planar jets. Here, the transition process in confined 2D jets o…
An efficient mass-preserving interface-correction level set/ghost fluid method for droplet suspensions under depletion forces
Zhouyang Ge, Jean-Christophe Loiseau, Outi Tammisola +1
Aiming for the simulation of colloidal droplets in microfluidic devices, we present here a numerical method for two-fluid systems subject to surface tension and depletion forces am…
Constrained Sparse Galerkin Regression
Jean-Christophe Loiseau, Steven L. Brunton
Although major advances have been achieved over the past decades for the reduction and identification of linear systems, deriving nonlinear low-order models still is a chal- lengin…
Simplified TPLS as a learning tool for high-performance computational fluid dynamics
James Fannon, Jean-Christophe Loiseau, Prashant Valluri +2
We introduce a modified and simplified version of the pre-existing fully parallelized three-dimensional Navier--Stokes flow solver known as TPLS. We demonstrate how the simplified…