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20152026
most citedSparse reduced-order modeling : Sensor-based dynamics to full-state estimation

236 citations · 746 across the 18 of their papers we have counts for

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12 papers · 1 filter

physics.flu-dyn2025

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…

physics.flu-dyn2023★ 1 cited

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…

physics.flu-dyn2022★ 1 cited

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…

physics.flu-dyn2021★ 56 cited

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…

physics.flu-dyn2021

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.…

physics.flu-dyn2019★ 49 cited

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…

physics.flu-dyn2018

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…

physics.flu-dyn2017★ 236 cited

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…

physics.flu-dyn2017★ 21 cited

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…

physics.flu-dyn2017★ 6 cited

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…

physics.flu-dyn2017★ 57 cited

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…

physics.flu-dyn2016★ 6 cited

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…