MyTune

New paper: “Nonlinear electro-elastic finite element analysis with neural network constitutive models”

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New paper published in CMAME under the project “Multiphysics-Informed Design of Tunable Smart Materials”.  The present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the investigations, PANN models for electro-elastic material behavior at finite deformations are calibrated to different synthetically generated datasets describing the … Read more

New paper: “Gradient enhanced gaussian process regression for constitutive modelling in finite strain hyperelasticity”

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New paper published in CMAME under the project “Multiphysics-Informed Design of Tunable Smart Materials”. This paper introduces a metamodelling technique that leverages gradient-enhanced Gaussian process regression (also known as gradient-enhanced Kriging), effectively emulating the response of diverse hyperelastic strain energy densities. The approach adopted incorporates principal invariants as inputs for the surrogate of the strain energy density. This integration enables … Read more

New paper: “Learning nonlinear constitutive models in finite strain electromechanics with Gaussian process predictors” 

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The first paper authored by our PhD student, Alberto Pérez Escolar, under the project “Multiphysics-Informed Design of Tunable Smart Materials” has been published in CM. This paper introduces a gradient-enhanced Gaussian process metamodel designed to emulate homogenized nonlinear electromechanical constitutive models. The methodology, implemented entirely using Julia, incorporates principal invariants as inputs for the surrogate … Read more