output
20092025
most citedThermodynamics-based Artificial Neural Networks for constitutive modeling

421 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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10 papers · 1 filter

cs.CE2024★ 15 cited

Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches

Martin Zlatić, Felipe Rocha, Laurent Stainier +1

We present a comparison between two approaches to modelling hyperelastic material behaviour using data. The first approach is a novel approach based on Data-driven Computational Me…

cs.CE2022★ 63 cited

Evolution TANN and the identification of internal variables and evolution equations in solid mechanics

Filippo Masi, Ioannis Stefanou

Data-driven and deep learning approaches have demonstrated to have the potential of replacing classical constitutive models for complex materials. Yet, the necessity of structuring…

cs.CE2021★ 17 cited

Lipschitz regularization for fracture: the Lip-field approach

N. Chevaugeon, N. Moes

The Lip-field approach is a new regularization method for softening material material models. It was presented first in a previous paper providing one-dimensional simulations for d…

cs.CE2021★ 13 cited

The eXtreme Mesh deformation approach (X-MESH) for the Stefan phase-change model

Nicolas Moes, Jean-Francois Remacle, Jonathan Lambrechts +2

The eXtreme Mesh deformation approach (X-MESH) is a new paradigm to follow sharp interfaces without remeshing and without changing the mesh topology. Even though the mesh does not…

cs.CE2021★ 19 cited

Lipschitz regularization for softening material models: the Lip-field approach

Nicolas Moes, Nicolas Chevaugeon

Softening material models are known to trigger spurious localizations.This may be shown theoretically by the existence of solutions with zero dissipation when localization occurs a…

cs.CE2020★ 66 cited

Efficient Data Structures for Model-free Data-Driven Computational Mechanics

Robert Eggersmann, Laurent Stainier, Michael Ortiz +1

The data-driven computing paradigm initially introduced by Kirchdoerfer and Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from materi…

cs.CE2020★ 143 cited

Data-driven fracture mechanics

Pietro Carrara, Laura De Lorenzis, Laurent Stainier +1

We present a new data-driven paradigm for variational brittle fracture mechanics. The fracture-related material modeling assumptions are removed and the governing equations stemmin…

cs.CE2020★ 81 cited

Model-free Data-Driven Computational Mechanics Enhanced by Tensor Voting

Robert Eggersmann, Laurent Stainier, Michael Ortiz +1

The data-driven computing paradigm initially introduced by Kirchdoerfer & Ortiz (2016) is extended by incorporating locally linear tangent spaces into the data set. These tangent s…

cs.CE2019★ 119 cited

Model-Free Data-Driven Methods in Mechanics: Material Data Identification and Solvers

Laurent Stainier, Adrien Leygue, Michael Ortiz

This paper presents an integrated model-free data-driven approach to solid mechanics, allowing to perform numerical simulations on structures on the basis of measures of displaceme…

cs.CE2018★ 303 cited

Model-Free Data-Driven Inelasticity

Robert Eggersmann, Trenton Kirchdoerfer, Stefanie Reese +2

We extend the Data-Driven formulation of problems in elasticity of Kirchdoerfer and Ortiz (2016) to inelasticity. This extension differs fundamentally from Data-Driven problems in…