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SUMMARY:Machine learning based plasticity modeling
DTSTART:20211209T160000
DTEND:20211209T170000
DTSTAMP:20260406T144354Z
UID:4f36c76d3179e124c23f5b6aceb91daf7a0d4cd040a2a1a6ac59f460
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Dirk Mohr - ETHZ\nAbstract: Machine learning offers a d
 ata-driven approach to the development of constitutive models as an altern
 ative to classical physics-based modeling.  Recent applications of machin
 e learning in the context of metal plasticity are presented ranging from t
 emperature and rate dependent hardening laws to 3D constitutive models for
  anisotropic solids. In addition to developing mechanics-specific neural n
 etwork architectures\, new robot-assisted experimental procedures are pres
 ented that generate “big data” for the identification of machine-learn
 ing based plasticity and failure models from experiments.
LOCATION:https://ethz.zoom.us/j/94817809233?pwd=N0pzbnQwSFFTQnVPcVR3SkNrd2
 9OQT09
STATUS:CONFIRMED
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