220 citations · 234 across the 5 of their papers we have counts for
5 papers
Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids
Hongyi Xiao, Ge Zhang, Entao Yang +5
All solids yield under sufficiently high mechanical loads. Below yield, the mechanical responses of all disordered solids are nearly alike, but above yield every different disorder…
Structuro-elasto-plasticity (StEP) model for plasticity in disordered solids
Ge Zhang, Hongyi Xiao, Entao Yang +5
Elastoplastic lattice models for the response of solids to deformation typically incorporate structure only implicitly via a local yield strain that is assigned to each site. Howev…
Tilting the playing field: Dynamical loss functions for machine learning
Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz +1
We show that learning can be improved by using loss functions that evolve cyclically during training to emphasize one class at a time. In underparameterized networks, such dynamica…
Interplay of rearrangements, strain, and local structure during avalanche propagation
Ge Zhang, Sean Ridout, Andrea J. Liu
Jammed soft disks exhibit avalanches of particle rearrangements under quasistatic shear. We follow the avalanches using steepest descent to decompose them into individual localized…
Predicting plasticity in disordered solids from structural indicators
D. Richard, M. Ozawa, S. Patinet +16
Amorphous solids lack long-range order. Therefore identifying structural defects -- akin to dislocations in crystalline solids -- that carry plastic flow in these systems remains a…