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Title
Discovering plasticity models without stress data
Authors
Keywords
-
Journal
npj Computational Materials
Volume 8, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-04-28
DOI
10.1038/s41524-022-00752-4
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- 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
- Extension of the virtual fields method to elasto-plastic material identification with cyclic loads and kinematic hardening
- (2010) F. Pierron et al. INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
- 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
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