Johann Guilleminot
Duke University
Elasticity (economics)Statistical modelStatistical physicsRandomnessMathematical optimizationStochastic modellingRandom fieldMathematical analysisInverse problemGaussianMaterials scienceApplied mathematicsUncertainty quantificationMathematicsHomogenization (chemistry)Computer scienceProbabilistic logicInformation theoryMolecular dynamicsRandom matrixPrinciple of maximum entropy
89Publications
18H-index
802Citations
Publications 87
Newest
#1Hao Zhang (Duke University)
#2Johann Guilleminot (Duke University)H-Index: 18
Last. Luis J. Gomez (Purdue University)
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Abstract null null We present a stochastic modeling framework to represent and simulate spatially-dependent geometrical uncertainties on complex geometries. While the consideration of random geometrical perturbations has long been a subject of interest in computational engineering, most studies proposed so far have addressed the case of regular geometries such as cylinders and plates. Here, standard random field representations, such as Karhunen–Loeve expansions, can readily be used owing, in pa...
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#1V-H. Trinh (Le Quy Don Technical University)
#2Johann Guilleminot (Duke University)H-Index: 18
Last. Camille Perrot (CNRS: Centre national de la recherche scientifique)H-Index: 13
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Abstract null null The acoustic properties of composite structures made of a perforated panel, an air gap, and a porous layer, can be studied numerically by a combined use of ad hoc optimization and sensitivity analysis methods. The methodology is briefly described and is systematically applied to a series of multi-layer configurations under manufacturing constraints. We specifically consider a foam layer of constant thickness presenting three different degrees of reticulations (pore opening). F...
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#1Cong Truc Nguyen (University of Paris)H-Index: 1
#2Johann Guilleminot (Duke University)H-Index: 18
Last. Camille Perrot (University of Paris)H-Index: 13
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1 CitationsSource
#1Tianchen Hu (Duke University)H-Index: 4
#2Johann Guilleminot (Duke University)H-Index: 18
Last. John E. Dolbow (Duke University)H-Index: 36
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Abstract We present a new derivation for a phase-field model of cohesive fracture that allows for fully-damaged surfaces to properly transmit tractions under frictionless contact conditions. The model is derived from an energy minimization standpoint, and the governing equations are presented in a general Allen–Cahn form, unifying both brittle and cohesive fracture models. A novel elastic energy split is proposed to enforce frictionless contact conditions along the regularized crack set. A fixed...
9 CitationsSource
#1Johann Guilleminot (Duke University)H-Index: 18
#2John E. Dolbow (Duke University)H-Index: 36
Abstract A data-driven framework for the enhancement of fracture paths in random heterogeneous microstructures is presented. The approach relies on the combination of manifold learning, introduced to explore the geometrical structure exhibited by crack patterns and achieve efficient dimensionality reduction, and a posteriori crack path reconstruction, defined through a Markovianization. The proposed methodology enables the generation of new crack patterns, the underlying structure and dynamical ...
13 CitationsSource
#1Johann Guilleminot (Duke University)H-Index: 18
Abstract The proper representation of random physical quantities and system parameter uncertainties is a key ingredient of predictive science. This modeling aspect must ensure, in particular, that all samples drawn from the stochastic model satisfy the requirements raised by the mathematical analysis of the associated boundary value problem. In addition, the model is typically required to mimic physics-based constraints, ranging from the prescription of material symmetries to the multiscale-infe...
4 CitationsSource
#1Darith Anthony Hun (University of Paris)H-Index: 1
#2Johann Guilleminot (Duke University)H-Index: 18
Last. Michel Bornert (University of Paris)H-Index: 35
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A stochastic approach to model crack propagation in random heterogeneous media, using mesoscopic representations of elastic and fracture properties, is presented. In order to obtain reference results, Monte-Carlo simulations are first conducted on microstructural samples in which a pre-existing crack is propagated by means of a phase-field approach. These computations are used to estimate the subscale-induced randomness on the macroscopic response of the domain. Mesoscopic descriptors are then i...
14 CitationsSource
#1Haoran Wang (Duke University)H-Index: 1
#2Johann Guilleminot (Duke University)H-Index: 18
Last. Christian Soize (University of Paris)H-Index: 56
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Abstract A methodology enabling the robust treatment of model-form uncertainties in molecular dynamics simulations is proposed. The approach consists in properly randomizing a reduced-order basis, obtained by the method of snapshots in the configuration space. A multi-step strategy to identify the hyperparameters in the stochastic reduced-order basis is further introduced. The relevance of the framework is finally demonstrated by characterizing various types of modeling errors associated with mo...
6 CitationsSource
#1Haoran Wang (Duke University)H-Index: 1
#2Johann Guilleminot (Duke University)H-Index: 18
Last. Christian SoizeH-Index: 56
view all 3 authors...