maximum-entropy principle

Consistent and Stable Meshfree Galerkin Methods Using The Virtual Element Decomposition

July 23, 2017
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A talk given by A. Ortiz-Bernardin at POEMS 2017: Workshop on Polytopal Element Methods in Mathematics and Engineering, July 6, 2017, Milan, Italy. Consistent and Stable Meshfree Galerkin Methods Using The Virtual Element Decomposition Alejandro Ortiz-Bernardin Department of Mechanical Engineering, University of Chile Av. Beauchef 851, Santiago, 8370456, Chile aortizb@ing.uchile.cl ABSTRACT In the numerical solution

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Consistent, Accurate, and Stable Meshfree Galerkin Methods Based on The Virtual Element Decomposition

May 9, 2015
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A talk given by A. Ortiz-Bernardin at the 1st Pan-American Congress on Computational Mechanics, April 27 – 29, 20145, Buenos Aires, Argentina. Consistent, Accurate, and Stable Meshfree Galerkin Methods Based on The Virtual Element Decomposition Alejandro Ortiz-Bernardin*, Alessandro Russo†, Natarajan Sukumar‡ * Department of Mechanical Engineering, University of Chile Av. Beauchef 850, Santiago, 8370448, Chile

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Structural damage assessment using linear approximation with maximum entropy and transmissibility data

April 13, 2015
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Mechanical Systems and Signal Processing Vol. 54-55, pp. 210-223, 2015 V. Meruane, A. Ortiz-Bernardin Abstract Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in structural damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and the large number of parameters that need to

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Paper Accepted: Structural Damage Assessment Using Linear Approximation with Maximum Entropy and Transmissibility Data

August 23, 2014
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Paper Accepted for Publication in Mechanical Systems and Signal Processing V. Meruane, A. Ortiz-Bernardin, “Structural damage assessment using linear approximation with maximum entropy and transmissibility data.” ABSTRACT Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in structural damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the

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Vibration-based damage assessment using linear approximation with maximum entropy

July 22, 2014
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14th Pan-American Congress of Applied Mechanics – PACAM XIV March 24-28, 2014, Santiago, Chile Proceedings of PACAM XIV V. Meruane and A. Ortiz-Bernardin Abstract Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in vibration-based damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and

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A maximum entropy approach to assess debonding in honeycomb aluminum plates

May 23, 2014
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Entropy Vol. 16, No. 5, pp. 2869-2889, 2014 V. Meruane, V. del Fierro, A. Ortiz-Bernardin Abstract Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of

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Maximum-entropy meshfree method for compressible and near-incompressible elasticity

November 25, 2013
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Computer Methods in Applied Mechanics and Engineering Vol. 119, No. 25-28, pp. 1859-1871, 2010 A. Ortiz , M.A. Puso, N. Sukumar Abstract Numerical integration errors and volumetric locking in the near-incompressible limit are two outstanding issues in Galerkin-based meshfree computations. In this paper, we present a modified Gaussian integration scheme on background cells for meshfree

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Maximum-entropy meshfree method for incompressible media problems

November 25, 2013
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Finite Elements in Analysis and Design Vol. 47, No. 6, pp. 572-585, 2011 A. Ortiz , M.A. Puso, N. Sukumar Abstract A novel maximum-entropy meshfree method that we recently introduced in Ortiz et al. (2010) [1] is extended to Stokes flow in two dimensions and to three-dimensional incompressible linear elasticity. The numerical procedure is aimed

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Paper Submitted: Damage Identification Using Linear Approximation with Maximum-Entropy and Transmissibility Data

September 19, 2013
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V. Meruane, A. Ortiz-Bernardin, “Damage identification using linear approximation with maximum-entropy and transmissibility data,” submitted. ABSTRACT Supervised learning algorithms have been proposed as a suitable alternative to model updating methods in damage assessment, being Artificial Neural Networks the most frequently used. Notwithstanding, the slow learning speed and the large number of parameters that need to

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