Illusion et Depiction: La Surface Invisible

Nous défendons la thèse selon laquelle les images sont phénoménalement transparentes : nous ne voyons (quasiment) jamais leur surface mais seulement ce que les images dépeignent, ce qui implique que notre expérience des images est fondamentalement une illusion. Cette thèse s’oppose à celle de R. Wollheim, qui fait aujourd’hui figure de position standard, selon laquelle nous percevons la surface et le depictum. Une même expérience perceptive, selon nous, ne peut avoir deux objets ou deux aspects. En ce sens, nous sommes plus proche de la position de E.H. Gombrich : nous percevons soit la surface, soit le depictum, mais jamais les deux. Nous cherchons toutefois à la radicaliser en soutenant qu’il est finalement très rare de percevoir la surface de l’image

Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution

In this article, we present a novel data assimilation strategy in pore-scale imaging and demonstrate that this makes it possible to robustly address reactive inverse problems incorporating Uncertainty Quantification (UQ). Pore-scale modeling of reactive flow offers a valuable opportunity to investigate the evolution of macro-scale properties subject to dynamic processes. Yet, they suffer from imaging limitations arising from the associated X-ray microtomography (X-ray microCT) process, which induces discrepancies in the properties estimates. Assessment of the kinetic parameters also raises challenges, as reactive coefficients are critical parameters that can cover a wide range of values. We account for these two issues and ensure reliable calibration of pore-scale modeling, based on dynamical microCT images, by integrating uncertainty quantification in the workflow. The present method is based on a multitasking formulation of reactive inverse problems combining data-driven and physics-informed techniques in calcite dissolution. This allows quantifying morphological uncertainties on the porosity field and estimating reactive parameter ranges through prescribed PDE models with a latent concentration field and dynamical microCT. The data assimilation strategy relies on sequential reinforcement incorporating successively additional PDE constraints. We guarantee robust and unbiased uncertainty quantification by straightforward adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), ensuring reliable micro-porosity changes during geochemical transformations. We demonstrate successful Bayesian Inference in 1D+Time and 2D+Time calcite dissolution based on synthetic microCT images with meaningful posterior distribution on the reactive parameters and dimensionless numbers

Spatially distributed control for optimal drag reduction of the flow past a circular cylinder

International audienceWe report high drag reduction in Direct Numerical Simulations of controled flows past circular cylinders at Reynolds numbers of 300 and 1000. The flow is controlled by the azimuthal component of the tangential velocity of the cylinder surface. Starting from a spanwise uniform velocity profile that leads to high drag reduction, the optimization procedure identifies, for the same energy input, spanwise varying velocity profiles that lead to higher drag reduction. The three dimensional variations of the velocity field, corresponding to the modes A and B of three dimensional wake instabilities, are largely responsible for this drag reduction. The spanwise, wall velocity variations introduce streamwise vortex braids in the wake that are responsible for reducing the drag induced by the primary spanwise vortices shed by the cylinder. The results demonstrate that extending two dimensional controllers to three-dimensional flows is not optimal as three dimensional control strategies can lead efficiently to higher drag reduction

Permeability estimation using vortex-based method and synthetic samples from X-ray micro-CT

International audienc

Mucus and ciliated cells of human lung : splitting strategies for particle methods and 3D stokes flows

Lung walls are covered by a film of mucus, whose motility is fundamental for a healthy behavior. Indeed, mucus traps inhaled aerosols (bacteria, dust, ...), and moves from smallest to largest airways, until it reaches esophagus where is it swallowed or expectorated. A lot of biological parameters are responsible for mucus motion [6], such as the vibrations of ciliated cells covering lung walls (cilia height, frequency, ...), mucus/air interaction, water saturation in mucin network, mucus thickness

Effective viscosity of a random mixture of fluids

International audienceWe propose an estimation of the effective viscosity of a random mixture of Newtonian fluids that ignores capillary effects. The local viscosity of the mixture is assumed to be a random function of the position. Using perturbation expansions up to the second order, the resulting formula can be recast under the form of a simple power averaging mixing low. Numerical tests are used to assess the validity of the formula and the range of its applicability

Spatially distributed control for optimal drag reduction of the flow past a circular cylinder

We report high drag reduction in direct numerical simulations of controlled flows past circular cylinders at Reynolds numbers of 300 and 1000. The flow is controlled by the azimuthal component of the tangential velocity of the cylinder surface. Starting from a spanwise-uniform velocity profile that leads to high drag reduction, the optimization procedure identifies, for the same energy input, spanwise-varying velocity profiles that lead to higher drag reduction. The three-dimensional variations of the velocity field, corresponding to modes A and B of three-dimensional wake instabilities, are largely responsible for this drag reduction. The spanwise wall velocity variations introduce streamwise vortex braids in the wake that are responsible for reducing the drag induced by the primary spanwise vortices shed by the cylinder. The results demonstrate that extending two-dimensional controllers to three-dimensional flows is not optimal as three-dimensional control strategies can lead efficiently to higher drag reductio

Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

In this paper, we present a novel methodology for automatic adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), and we demonstrate that this makes it possible to robustly address multi-objective and multi-scale problems. BPINNs are a popular framework for data assimilation, combining the constraints of Uncertainty Quantification (UQ) and Partial Differential Equation (PDE). The relative weights of the BPINN target distribution terms are directly related to the inherent uncertainty in the respective learning tasks. Yet, they are usually manually set a-priori, that can lead to pathological behavior, stability concerns, and to conflicts between tasks which are obstacles that have deterred the use of BPINNs for inverse problems with multi-scale dynamics. The present weighting strategy automatically tunes the weights by considering the multi-task nature of target posterior distribution. We show that this remedies the failure modes of BPINNs and provides efficient exploration of the optimal Pareto front. This leads to better convergence and stability of BPINN training while reducing sampling bias. The determined weights moreover carry information about task uncertainties, reflecting noise levels in the data and adequacy of the PDE model. We demonstrate this in numerical experiments in Sobolev training, and compare them to analytically $\epsilon$-optimal baseline, and in a multi-scale Lokta-Volterra inverse problem. We eventually apply this framework to an inpainting task and an inverse problem, involving latent field recovery for incompressible flow in complex geometries

Numerical and experimental investigation of mucociliary clearance breakdown in cystic fibrosis

The human tracheobronchial tree surface is covered with mucus. A healthy mucus is a heterogeneous material flowing toward the esophagus and a major defense actor against local pathogen proliferation and pollutant deposition. An alteration of mucus or its environment such as in cystic fibrosis dramatically impacts the mucociliary clearance. In the present study, we investigate the mechanical organization and the physics of such mucus in human lungs by means of a joint experimental and numerical work. In particular, we focus on the influence of the shear-thinning mucus mobilized by a ciliated epithelium for mucociliary clearance. The proposed robust numerical method is able to manage variations of more than 5 orders of magnitude in the shear rate and viscosity. It leads to a cartography that allows to discuss major issues on defective mucociliary clearance in cystic fibrosis. Furthermore, the computational rheological analysis based on measurements shows that cystic fibrosis shear-thinning mucus tends to aggregate in regions of lower clearance. Yet, a rarefaction of periciliary fluid has a greater impact than the mucus shear-thinning effects