Papers

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

DeepONet: a deep-learning-based framework for approximating linear and nonlinear operators

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Delighted to convene the inaugural meeting of the research team for the coordinated POTENTIAL project in Cartagena. Our discussions on electromechanics, optimal control, and deep-learning algorithms were exceptionally fruitful, fostering an atmosphere of collaboration and innovation. Excited to witness the promising developments that will emerge as we continue to work together towards our shared objectives.Grant … 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

New book on “Optimal Control of Partial Differential Equations under Uncertainty”

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The book “Optimal Control of PDEs under Uncertainty”  by Jesús Martínez-Frutos and Francisco Periago has been published in BCAM SpringerBriefs .This book gives a direct and comprehensive introduction to the theoretical and numerical concepts in the emergent field of optimal control of partial differential equations (PDEs) under uncertainty. The main objective of the book is to provide graduate students … Read more