Publications (21)
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…
Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wings
Rahul Sundar, Didier Lucor, Sunetra Sarkar
For a data-driven and physics combined modelling of unsteady flow systems with moving immersed boundaries, Sundar {\it et al.} introduced an immersed boundary-aware (IBA) framework…
Understanding the training of PINNs for unsteady flow past a plunging foil through the lens of input subdomain level loss function gradients
Rahul Sundar, Didier Lucor, Sunetra Sarkar
Recently immersed boundary method-inspired physics-informed neural networks (PINNs) including the moving boundary-enabled PINNs (MB-PINNs) have shown the ability to accurately reco…
Digital twin-based hybrid framework for steam generator clogging prognostics
Edgar Jaber, Emmanuel Remy, Vincent Chabridon +5
We present a hybrid framework to support prognostics of the clogging degradation phenomenon in tube support plates for digital twins of steam generators in pressurized water reacto…
Fusion of heterogeneous data for robust degradation prognostics
Edgar Jaber, Emmanuel Remy, Vincent Chabridon +2
Assessing the degradation state of an industrial asset first requires evaluating its current condition and then to project the forecast model trajectory to a predefined prognostic…
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…
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…
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…
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 fun…
A PINN Methodology for Temperature Field Reconstruction in the PIV Measurement Plane: Case of Rayleigh-Bénard Convection
Marie-Christine Volk, Anne Sergent, Didier Lucor +3
We present a method to infer temperature fields from stereo particle-image velocimetry (PIV) data in turbulent Rayleigh-Bénard convection (RBC) using Physics-informed neural netwo…
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…
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…
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…
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…
Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows
Jiayi Cai, Pierre-Emmanuel Angeli, Jean-Marc Martinez +2
Several Tensor Basis Neural Network (TBNN) frameworks aimed at enhancing turbulence RANS modeling have recently been proposed in the literature as data-driven constitutive models f…
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…
Sensitivity Analyses of a Multi-Physics Long-Term Clogging Model For Steam Generators
Edgar Jaber, Vincent Chabridon, Emmanuel Remy +4
Long-term operation of nuclear steam generators can result in the occurrence of clogging, a deposition phenomenon that may increase the risk of mechanical and vibration loadings on…
Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Edgar Jaber, Vincent Blot, Nicolas Brunel +6
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in indus…
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…