Neural network constitutive model for rate-dependent materials

By Sungmoon Jung, Jamshid Ghaboussi. 2006

196 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/7903fd711fd42a146ab9623e679f19b797975263

Citations in the corpus

• Machine learning strategies for systems with invariance properties (2016, unknown): “Subsequently, neural networks have been used to model the stress–strain relation in a variety of materials, including sand [18], viscoplastic materials [19], metal alloys [20], and concrete [21].”