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

Sparse identification of nonlinear dynamics for rapid model recovery

(2018) Markus Quade et al.   CHAOS

Thermodynamically consistent data-driven computational mechanics

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

Hidden physics models: Machine learning of nonlinear partial differential equations

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

Capturing anisotropic constitutive models with WYPiWYG hyperelasticity; and on consistency with the infinitesimal theory at all deformation levels

(2017) Erica De Rosa et al.   INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS

Machine learning of linear differential equations using Gaussian processes

(2017) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Inferring solutions of differential equations using noisy multi-fidelity data

(2017) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

kPCA-Based Parametric Solutions Within the PGD Framework

(2016) D. González et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

A Manifold Learning Approach for Integrated Computational Materials Engineering

(2016) E. Lopez et al.   ARCHIVES OF COMPUTATIONAL 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

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