activity
20002025
most citedStrain rate dependency of dislocation plasticity

371 citations · 1.7k across the 60 of their papers we have counts for

collaborators

62 papers

cond-mat.mtrl-sci2025

Dislocations in the elastic fields of randomly distributed defects

Ronghai Wu, Michael Zaiser

In recent years, the behavior of dislocations in random solid solutions has received renewed interest, and several models have been discussed where random alloys are treated as eff…

cond-mat.soft2023

Multiscale modeling of dislocations: Combining peridynamics with gradient elasticity

Jonas Ritter, Michael Zaiser

Modeling dislocations is an inherently multiscale problem as one needs to simultaneously describe the high stress fields near the dislocation cores, which depend on atomistic lengt…

cond-mat.mtrl-sci2023

Failure precursors and failure mechanisms in hierarchically patterned paper sheets in tensile and creep loading

Mahshid Pournajar, Tero Mäkinen, Seyyed Ahmad Hosseini +3

Quasi-brittle materials endowed with (statistically) self-similar hierarcical microstructures show distinct failure patterns that deviate from the standard scenario of damage accum…

cond-mat.mtrl-sci2023★ 11 cited

Effects of disorder on deformation and failure of brittle porous materials

Jonas Ritter, Shucheta Shegufta, Michael Zaiser

The mechanical behavior of porous materials depends strongly on porosity and pore geometry, but also on morphological parameters characterizing the spatial arrangement of pores. He…

cs.CE2022

An energetically consistent surface correction method for bond-based peridynamics

Jonas Ritter, Shucheta Shegufta, Paul Steinmann +1

A novel surface correction method is proposed for bond based peridynamics which ensures energy consistency with a classical reference body for general affine deformations. This met…

cond-mat.mtrl-sci2022★ 1 cited

Predicting Creep Failure by Machine Learning -- Which Features Matter?

Stefan Hiemer, Paolo Moretti, Stefano Zapperi +1

Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated f…