HAL-Polytechnique
Not a member yet
51406 research outputs found
Sort by
Search for a standard model-like Higgs boson in the mass range between 70 and 110 GeV in the diphoton final state in proton-proton collisions at = 13 TeV
No full textInternational audienceThe results of a search for a standard model-like Higgs boson decaying into two photons in the mass range between 70 and 110 GeV are presented. The analysis uses the data set collected by the CMS experiment in proton-proton collisions at = 13 TeV corresponding to integrated luminosities of 36.3 fb, 41.5 fb and 54.4 fb during the 2016, 2017, and 2018 LHC running periods, respectively. No significant excess over the background expectation is observed and 95% confidence level upper limits are set on the product of the cross section and branching fraction for decays of an additional Higgs boson into two photons. The maximum deviation with respect to the background is seen for a mass hypothesis of 95.4 GeV with a local (global) significance of 2.9 (1.3) standard deviations. The observed upper limit ranges from 15 to 73 fb
Learning signals defined on graphs with optimal transport and Gaussian process regression
No full textInternational audienceIn computational physics, machine learning has now emerged as a powerful complementary tool to explore efficiently candidate designs in engineering studies. Outputs in such supervised problems are signals defined on meshes, and a natural question is the extension of general scalar output regression models to such complex outputs. Changes between input geometries in terms of both size and adjacency structure in particular make this transition non-trivial. In this work, we propose an innovative strategy for Gaussian process regression where inputs are large and sparse graphs with continuous node attributes and outputs are signals defined on the nodes of the associated inputs. The methodology relies on the combination of regularized optimal transport, dimension reduction techniques, and the use of Gaussian processes indexed by graphs. In addition to enabling signal prediction, the main point of our proposal is to come with confidence intervals on node values, which is crucial for uncertainty quantification and active learning. Numerical experiments highlight the efficiency of the method to solve real problems in fluid dynamics and solid mechanics
Estimating elastic and thermal contributions to lattice strains from operando X-ray diffraction measurements using fast simulations
No full textInternational audienceLattice strains obtained from operando synchrotron X-ray diffraction measurements during metal additive manufacturing are being increasingly used to estimate temperature evolution during the process. At the minimum, these transient lattice strains have contributions from thermal and elastic strains. Temperature estimates from lattice strains have thus far been extracted assuming that elastic strains are negligible in comparison to thermal strains at high temperatures when the heat source is close to the probed region. However, such an assumption may not only lead to inaccuracies in estimating temperature but also fail to correctly estimate the non-negligible stress evolution occurring at moderate to low temperatures as the heat source moves away. Numerical simulations can be used to predict lattice strains but these predictions are necessarily different from experimental measures.This work proposes an experimentally corrected numerical approach to improve simulation predictions. It involves first using a recently developed fast numerical thermomechanics model to predict lattice strains. Then, the predicted thermal and elastic strains are corrected using a minimization procedure under the strict constraint that the predicted lattice strains are strictly equal to the measured ones, thus improving the original estimates. This strategy is demonstrated for operando synchrotron X-ray diffraction measurements during directed energy deposition of a thin wall made from 316L stainless steel, which exhibits negligible solid-state phase transformations. Following validation, the corrected thermal and elastic strains are used to estimate temperature and stress evolution and study the difference in temperature and heating/cooling rate prediction caused by neglecting elastic strains.</p
On the strong law of large numbers and Llog L condition for supercritical general branching processes
No full textWe consider branching processes for structured populations: each individual is characterized by a type or trait which belongs to a general measurable state space. We focus on the supercritical recurrent case, where the population may survive and grow and the trait distribution converges. The branching process is then expected to be driven by the positive triplet of first eigenvalue problem of the first moment semigroup. Under the assumption of convergence of the renormalized semigroup in weighted total variation norm, we prove strong convergence of the normalized empirical measure and non-degeneracy of the limiting martingale. Convergence is obtained under an Llog L condition which provides a Kesten-Stigum result in infinite dimension and relaxes the uniform convergence assumption of the renormalized first moment semigroup required in the work of Asmussen and Hering in 1976. The techniques of proofs combine families of martingales and contraction of semigroups and the truncation procedure of Asmussen and Hering. We also obtain L^1 convergence of the renormalized empirical measure and contribute to unifying different results in the literature. These results greatly extend the class of examples where a law of large numbers applies, as we illustrate it with absorbed branching diffusion, the house of cards model and some growth-fragmentation processes
IPANEMAP Suite: a pipeline for probing-informed RNA structure modeling
No full textInternational audienceIn addition to their sequence, multiple functions of RNAs are encoded within their structure, which is often difficult to solve using physico-chemical methods. Incorporating low resolution experimental data such as chemical probing into computational prediction significantly enhances RNA structure modeling accuracy. While medium and high-throughput RNA structure probing techniques are widely accessible, the subsequent analysis process can be cumbersome, involving multiple software and manual data manipulation. In addition, the relevant interpretation of the data requires proper parametrization of the software and a strict consistency in the analysis pipeline. To streamline such workflows, we introduce IPANEMAP Suite, a comprehensive platform that guides users from chemical probing raw data to visually informative secondary structure models. IPANEMAP Suite seamlessly integrates various experimental data sets and facilitates comparative analysis of RNA structures under different conditions (footprinting), aiding in the study of protein or small molecule interactions with RNA. Here we show that the unique ability of IPANEMAP suite to perform integrative modeling using several chemical probing datasets with phylogenetic data can be instrumental to obtain accurate secondary structure models. The platform's project-based approach ensures full-traceability and generates publication-quality outputs, simplifying the entire RNA structure analysis process. IPANEMAP Suite is freely available at https://github.com/Sargueil-CiTCoM/ipasuite under an GPL-3.0 licence
Adjoint-based optimization for non-linear inverse problems with high-order discretization of the compressible RANS equations
No full textInternational audienceThis work presents an adjoint-based strategy to solve non-linear inverse problems discretized with high-order numerical methods. The inverse problem is defined here based on the optimization of a control parameter to minimize a cost-functional subject to the compressible RANS equations discretized with the modal discontinuous Galerkin (DG) method. The distributed control parameter is searched in the DG function space and the discrete adjoint approach, consistent with the formal problem, is used to compute the derivative of the cost function in the optimization process. The linearization of the cost-functional and of the governing equations, the expression of the gradient, as well as the numerical strategy to efficiently solve the adjoint system with flexible inner-outer GMRES solvers have been detailed. In the case of a strongly under-determined problem, regularization techniques based on the penalization of the norm of the control parameter have been introduced. The methodology is illustrated on the case of a data-assimilation (DA) problem, which aims at minimizing the discrepancy of (sparse) high-fidelity measurements with the solution of the RANS equations corrected by four different control parameters. The optimization strategy is tested progressively with measurements on the full computational domain (abundant measurements) and solid wall boundaries (sparse measurements). First, a laminar flow around a cylinder is used to validate the inverse problem resolution with a DG discretization of different approximation orders. Subsequently, results regarding a turbulent flow around a square cylinder allow to compare the optimization convergence of each corrective parameters with abundant measurements. Finally, a shock-wave/turbulent boundary-layer interaction configuration is considered. Great correction of the velocity field is obtained with one of the proposed corrective term. In the case of abundant measurements it is also possible to get accurate correction of wall variables such as the skin-friction and pressure coefficient. Regularization of the optimal space, in case of sparse measurements, is attempt through penalization techniques
L'évolution de la motilité de gouttes actives est capturée par un modèle de marche aléatoire auto-évitante.
No full textInternational audienceIn living matter, concentration gradients of nutrients carve the motility of microorganisms in a heterogeneous environment. Here, we use swimming droplets as a model system to study how swimmer-trail interactions guide locomotion. Combining experiments and theory, we show that our non-Markovian droplet model quantitatively captures droplet motility. The two fit parameters provide the first estimate of the effective temperature arising from hydrodynamic flows and the coupling strength of the propulsion force. This framework is general and explains memory effects, droplet hovering, and enhanced collective motion
Sub-MHz homogeneous linewidth in epitaxial Y 2 O 3 : Eu 3+ thin film on silicon
No full textInternational audienceAbstract Thin films provide nanoscale confinement together with compatibility with photonic and microwave architectures, making them ideal candidates for chip-scale quantum devices. In this work, we propose a thin film fabrication approach yielding the epitaxial growth of Eu 3+ doped Y 2 O 3 on silicon. We combine two of the most prominent thin film deposition techniques: chemical vapor deposition (CVD) and molecular beam epitaxy (MBE). We report sub-megahertz optical homogeneous linewidths up to 8 K for the Eu 3+ dopants in the film, and lowest value of 270 kHz. This result constitutes a ten-fold improvement with respect to previous reports on the same material, opening promising perspectives for the development of scalable and compact quantum devices containing rare-earth ions
Influence of the mesh on the crack path in phase-field fracture simulations
No full textMeeting of the 10th GAMM workshop on phase-field modeling and the workshop Materials/Microstructure modelling: Analytics & Benchmarks organized and hosted by KIT with support by the DGM.International audienceOver the past 25 years, phase-field fracture models [1, 2] have become increasingly popular for modeling crack propagation. In particular, their (Γ-)convergence towards the Linear Elastic Fracture Mechanics (LEFM) provides strong theoretical foundations. Despite this popularity, limitedresearch has been conducted on how spatial discretization (e.g., mesh size, structure, and element geometry) affects the predicted crack path. This study addresses this gap from the perspective of the mechanical engineering community. We employ a benchmark problem inspired by the PureShear test [3] (also called strip specimen), involving an infinite strip with an initial horizontal edge crack located above the specimen center and subjected to tensile loading. The crack path is expected to deviate towards the center of the specimen exponentially. This result has been recovered using an incremental crack propagation solver based on LEFM, which serves as our reference. Phase-field fracture simulations, performed using the Finite Element Method, are then carried out. Different meshes (varying mesh size, structured/unstructured, and element geometry) are used in the simulations to assess their influence on the crack path. The bias induced by the mesh is evaluated by comparing the phase field simulation results with the reference. The final goal of this study is to provide recommendations to avoid, or at least mitigate, any bias induced by spatial discretization
Cold plasma treatment of cholangiocarcinoma: investigating skin tissue as a barrier to electric field propagation and reactive species diffusion
No full textJe procède à cette soumission dans le cadre de la présentation d'un poster.International audienc