Close-packing transitions in clusters of Lennard-Jones spheres

The structures of clusters of spherical and homogeneous particles are investigated using a combination of global optimization methods. The pairwise potential between particles is integrated exactly from elementary Lennard-Jones interactions, and the use of reduced units allows us to get insight into the effects of the particle diameter. As the diameter increases, the potential becomes very sharp, and the cluster structure generally changes from icosahedral (small radius) to close-packed cubic (large radius), possibly through intermediate decahedral shapes. The results are interpreted in terms of the effective range of the potential

Maxiset comparisons of procedures, application to choosing priors in a Bayesian nonparametric setting

In this paper our aim is to provide tools for easily calculating the maxisets of several procedures. Then we apply these results to perform a comparison between several Bayesian estimators in a non parametric setting. We obtain that many Bayesian rules can be described through a general behavior such as being shrinkage rules, limited, and/or elitist rules. This has consequences on their maxisets which happen to be automatically included in some Besov or weak Besov spaces, whereas other properties such as cautiousness imply that their maxiset conversely contains some of the spaces quoted above. We compare Bayesian rules taking into account the sparsity of the signal with priors which are combination of a Dirac with a standard distribution. We consider the case of Gaussian and heavy tail priors. We prove that the heavy tail assumption is not necessary to attain maxisets equivalent to the thresholding methods. Finally we provide methods using the tree structure of the dyadic aspect of the multiscale analysis, and related to Lepki's procedure, achieving strictly larger maxisets than those of thresholding methods

From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning

Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly modified throughout the aircraft life cycle via the tuning of a single factor, enabling better fuel predictions. However this has limitations, in particular they do not reflect the evolution of each feature impacting the aircraft performance. Our goal here is to overcome this limitation. The key contribution of the present article is to foster the use of machine learning to leverage the massive amounts of data continuously recorded during flights performed by an aircraft and provide models reflecting its actual and individual performance. We illustrate our approach by focusing on the estimation of the drag and lift coefficients from recorded flight data. As these coefficients are not directly recorded, we resort to aerodynamics approximations. As a safety check, we provide bounds to assess the accuracy of both the aerodynamics approximation and the statistical performance of our approach. We provide numerical results on a collection of machine learning algorithms. We report excellent accuracy on real-life data and exhibit empirical evidence to support our modelling, in coherence with aerodynamics principles.Comment: Published in Data-Centric Engineerin

Systoles and diameters of hyperbolic surfaces

In this article we explore the relationship between the systole and the diameter of closed hyperbolic orientable surfaces. We show that they satisfy a certain inequality, which can be used to deduce that their ratio has a (genus dependent) upper bound.Comment: 13 pages, 9 figure

Combinatorial Mesh Optimization

International audienceA new mesh optimization framework for 3D triangular surface meshes is presented, which formulates the task as an energy minimization problem in the same spirit as in Hoppe et al. (SIGGRAPH’93: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, 1993). The desired mesh properties are controlled through a global energy function including data attached terms measuring the fidelity to the original mesh, shape potentials favoring high quality triangles, and connectivity as well as budget terms controlling the sampling density. The optimization algorithm modifies mesh connectivity as well as the vertex positions. Solutions for the vertex repositioning step are obtained by a discrete graph cut algorithm examining global combinations of local candidates.Results on various 3D meshes compare favorably to recent state-of-the-art algorithms. Applications consist in optimizing triangular meshes and in simplifying meshes, while maintaining high mesh quality. Targeted areas are the improvement of the accuracy of numerical simulations, the convergence of numerical schemes, improvements of mesh rendering (normal field smoothness) or improvements of the geometric prediction in mesh compression technique

Thermo-mechanical Analysis of Blister Damage in Eb-pvd Tbc System: Experiments and Modeling

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1D-mosaics grouping using lattice vector quantization for a video browsing application

International audience1D-mosaics have been introduced as a tool for structuring and navigation in video content. These objects can be con- sidered as the spatio-temporal signatures of the video shots. Our work aims at grouping automatically the video shots into scenes using these signatures. The original method is based on the tree-structured lattice vector quantization of the 1D-mosaics. Because of the hierarchical structure of the code-books, they can be compared progressively, and lattice use is time efficient. Indexing retrieval results are given for two video sequences, and different mosaics are successively compared to each other in order to assess the presented scheme's effectiveness

An end-to-end data-driven optimization framework for constrained trajectories

Abstract Many real-world problems require to optimize trajectories under constraints. Classical approaches are often based on optimal control methods but require an exact knowledge of the underlying dynamics and constraints, which could be challenging or even out of reach. In view of this, we leverage data-driven approaches to design a new end-to-end framework which is dynamics-free for optimized and realistic trajectories. Trajectories are here decomposed on function basis, trading the initial infinite dimension problem on a multivariate functional space for a parameter optimization problem. Then a maximum a posteriori approach which incorporates information from data is used to obtain a new penalized optimization problem. The penalized term narrows the search on a region centered on data and includes estimated features of the problem. We apply our data-driven approach to two settings in aeronautics and sailing routes optimization. The developed approach is implemented in the Python library PyRotor