101 citations · 276 across the 14 of their papers we have counts for
14 papers
Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil
Rahul Sundar, Dipanjan Majumdar, Didier Lucor +1
Recently, physics informed neural networks (PINNs) have been explored extensively for solving various forward and inverse problems and facilitating querying applications in fluid m…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Reynolds Stress Anisotropy Tensor Predictions for Turbulent Channel Flow using Neural Networks
Jiayi Cai, Pierre-Emmanuel Angeli, Jean-Marc Martinez +2
The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reyno…
Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion
Bastien X Nony, Mélanie Rochoux, Thomas Jaravel +1
Mapping near-field pollutant concentration is essential to track accidental toxic plume dispersion in urban areas. By solving a large part of the turbulence spectrum, large-eddy si…
Computational analysis of flow structures in turbulent ventricular blood flow associated with mitral valve intervention
Joel Kronborg, Frida Svelander, Samuel Eriksson-Lidbrink +4
Cardiac disease and clinical intervention may both lead to an increased risk for thrombosis events due to modified blood flow in the heart, and thereby a change in the mechanical s…
Uncertainty quantification of a thrombosis model considering the clotting assay PFA-100
Rodrigo Méndez Rojano, Mansur Zhussupbekov, James F. Antaki +1
Mathematical models of thrombosis are currently used to study clinical scenarios of pathological thrombus formation. Most of these models involve inherent uncertainties that must b…
Observation data compression for variational assimilation of dynamical systems
Sibo Cheng, Didier Lucor, Jean-Philippe Argaud
Accurate estimation of error covariances (both background and observation) is crucial for efficient observation compression approaches in data assimilation of large-scale dynamical…
Physics-aware deep neural networks for surrogate modeling of turbulent natural convection
Didier Lucor, Atul Agrawal, Anne Sergent
Recent works have explored the potential of machine learning as data-driven turbulence closures for RANS and LES techniques. Beyond these advances, the high expressivity and agilit…
A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping
Sibo Cheng, Jean-Philippe Argaud, Bertrand Iooss +2
An original graph clustering approach to efficient localization of error covariances is proposed within an ensemble-variational data assimilation framework. Here the localization t…
Background Error Covariance Iterative Updating with Invariant Observation Measures for Data Assimilation
Sibo Cheng, Jean-Philippe Argaud, Bertrand Iooss +2
In order to leverage the information embedded in the background state and observations, covariance matrices modelling is a pivotal point in data assimilation algorithms. These matr…
Reduced-order modeling of hemodynamics across macroscopic through mesoscopic circulation scales
Olivier Adjoua, Stéphanie Pitre-Champagnat, Didier Lucor
We propose a hemodynamic reduced-order model bridging macroscopic and meso-scopic blood flow circulation scales from arteries to capillaries. In silico tree like vascular geometrie…
Goal-oriented error control of stochastic system approximations using metric-based anisotropic adaptations
Jan Van Langenhove, Didier Lucor, Frédéric Alauzet +1
The simulation of complex nonlinear engineering systems such as compressible fluid flows may be targeted to make more efficient and accurate the approximation of a specific (scalar…
Uncertainty quantification of inflow boundary condition and proximal arterial stiffness coupled effect on pulse wave propagation in a vascular network
A Brault, L Dumas, D Lucor
SUMMARY This work aims at quantifying the effect of inherent uncertainties from cardiac output on the sensitivity of a human compliant arterial network response based on stochastic…
A stochastic view of isotropic turbulence decay
Marcello Meldi, Pierre Sagaut, Didier Lucor
A stochastic EDQNM approach is used to investigate self-similar decaying isotropic turbulence at high Reynolds number (). The realistic energy spectrum func…