Accelerating the distance-minimizing method for data-driven elasticity with adaptive hyperparameters

A k‐means clustering machine learning‐based multiscale method for anelastic heterogeneous structures with internal variables

(2022) Mohamed Amine Benaimeche et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Efficient data structures for model-free data-driven computational mechanics

(2021) Robert Eggersmann et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials

(2021) Mauricio Fernández et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Data-Driven Finite Elasticity

(2020) S. Conti et al.   ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS

A physics-constrained data-driven approach based on locally convex reconstruction for noisy database

(2020) Qizhi He et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A kernel method for learning constitutive relation in data-driven computational elasticity

(2020) Yoshihiro Kanno   JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS

On the micromechanics of deep material networks

(2020) Sebastian Gajek et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Variational framework for distance-minimizing method in data-driven computational mechanics

(2020) Lu Trong Khiem Nguyen et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A surrogate model for computational homogenization of elastostatics at finite strain using HDMR ‐based neural network

(2020) Vien Minh Nguyen‐Thanh et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Data-driven fracture mechanics

(2020) P. Carrara et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning

(2020) Kevin Linka et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Mixed-integer programming formulation of a data-driven solver in computational elasticity

(2019) Yoshihiro Kanno   Optimization Letters

Model-Free Data-Driven inelasticity

(2019) R. Eggersmann et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-Driven Problems in Elasticity

(2018) S. Conti et al.   ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS

A new reliability-based data-driven approach for noisy experimental data with physical constraints

(2018) Jacobo Ayensa-Jiménez et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

(2018) Kun Wang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-based derivation of material response

(2018) Adrien Leygue et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A data-driven approach to nonlinear elasticity

(2018) Lu Trong Khiem Nguyen et al.   COMPUTERS & STRUCTURES

Simple heuristic for data-driven computational elasticity with material data involving noise and outliers: a local robust regression approach

(2018) Yoshihiro Kanno   JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS

A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

(2018) Xiaoxin Lu et al.   COMPUTATIONAL MECHANICS

Data-driven computing in dynamics

(2017) T. Kirchdoerfer et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

A Manifold Learning Approach to Data-Driven Computational Elasticity and Inelasticity

(2016) Rubén Ibañez et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

Data-driven computational mechanics

(2016) T. Kirchdoerfer et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Computational homogenization of nonlinear elastic materials using neural networks

(2015) B. A. Le et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING