Unsupervised discovery of interpretable hyperelastic constitutive laws

Data-driven automated discovery of variational laws hidden in physical systems

(2020) Zhilong Huang et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Inverse-designed spinodoid metamaterials

(2020) Siddhant Kumar et al.   npj Computational Materials

Learning constitutive relations from indirect observations using deep neural networks

(2020) Daniel Z. Huang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Reactive SINDy: Discovering governing reactions from concentration data

(2019) Moritz Hoffmann et al.   JOURNAL OF CHEMICAL PHYSICS

Learning Corrections for Hyperelastic Models From Data

(2019) David González et al.   Frontiers in Materials

Data-driven discovery of PDEs in complex datasets

(2019) Jens Berg et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Model-Free Data-Driven inelasticity

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

Data-driven discovery of coordinates and governing equations

(2019) Kathleen Champion et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep learning predicts path-dependent plasticity

(2019) M. Mozaffar et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Data-Driven Problems in Elasticity

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

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

Thermodynamically consistent data-driven computational mechanics

(2018) David González et al.   CONTINUUM MECHANICS AND THERMODYNAMICS

Hybrid constitutive modeling: data-driven learning of corrections to plasticity models

(2018) Rubén Ibáñez et al.   International Journal of Material Forming

Sparse structural system identification method for nonlinear dynamic systems with hysteresis/inelastic behavior

(2018) Zhilu Lai et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven computing in dynamics

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

Data-driven discovery of partial differential equations

(2017) Samuel H. Rudy et al.   Science Advances

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

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

WYPIWYG hyperelasticity for isotropic, compressible materials

(2016) José Crespo et al.   COMPUTATIONAL MECHANICS

Data-driven computational mechanics

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

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The Virtual Fields Method for Extracting Constitutive Parameters From Full-Field Measurements: a Review

(2010) M. Grédiac et al.   STRAIN

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

A model of incompressible isotropic hyperelastic material behavior using spline interpolations of tension-compression test data

(2008) Theodore Sussman et al.   Communications in numerical methods in engineering