Mines ParisTech

About: Mines ParisTech is a education organization based out in Paris, France. It is known for research contribution in the topics: Finite element method & Microstructure. The organization has 6564 authors who have published 11676 publications receiving 359898 citations. The organization is also known as: École nationale supérieure des mines de Paris & École des mines de Paris.

Damage accumulation in a carbon/epoxy composite: Comparison between a multiscale model and computed tomography experimental results

Abstract: High-resolution computed tomography has been carried out for carbon/epoxy laminates loaded in situ to failure. The experimental data allows major damage mechanisms to be quantified in 3D, in an unambiguous and mechanically representative way, where previous experimental analysis is limited. A multi-scale model that predicts damage accumulation in tensile loaded composites is compared to the experimental analysis, to validate the underpinning assumptions within the model and overall performance. The model considers the random nature of fibre-strengths, stress transfer resulting from fibre breaks, fibre/matrix debonding and viscosity of the matrix. Assumptions within the model are made to reduce computational times whilst considering the microscopic behaviour of the whole structure. Both model and experimental results indicate failure of the composite progresses via single fibre breaks, which, at higher loads, evolve into clusters of broken fibres. The model resulted in reasonable predictions of the preceding damage accumulation and final failure load of the structure.

Mathematical model coupling phase transformations and temperature evolutions in steels

Abstract: A mathematical model for calculating phase transformations in steels during rapid heating and cooling is presented. It is based on a rule of additivity. The isothermal kinetics are modelled by Johnson-Mehl-Avrami law. The model describes the kinetics of austenitization during heating, the state of austenite at the end of heating (carbon content, grain size), the kinetics of transformations during cooling, the final microstructure and hardness. The model is worked out firstly on dilatometric specimens without thermal gradients in order to validate the modelling and the input data. Then the application of the model to massive cylinders heated up and cooled down with high thermal gradients is presented.

A new pairwise kernel for biological network inference with support vector machines

Abstract: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.