45 citations · 46 across the 3 of their papers we have counts for
3 papers
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…
Relating plasticity to dislocation properties by data analysis: scaling vs. machine learning approaches
Stefan Hiemer, Haidong Fan, Michael Zaiser
Plasticity modelling has long been based on phenomenological models based on ad-hoc assuption of constitutive relations, which are then fitted to limited data. Other work is based…
Predicting the failure of two-dimensional silica glasses
Francesc Font-Clos, Marco Zanchi, Stefan Hiemer +4
Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices…