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SUMMARY:Data-driven and Physics-aware Microstructural Modeling of Flowing 
 Complex Fluids - Michael Graham (University of Wisconsin-Madison)
DTSTART:20250910T121000Z
DTEND:20250910T125000Z
UID:TALK233287@talks.cam.ac.uk
DESCRIPTION:Flows used to process complex soft materials almost always inv
 olve complex deformations that cannot be captured in a rheometer but may p
 rofoundly influence the final microstructure and performance of the materi
 al.&nbsp\; Furthermore\, accurate first-principles models to relate flow\,
  microstructure\, and stress are unavailable for most complex fluids\, esp
 ecially when undergoing complex deformations. We describe a framework that
  uses machine learning and data assimilation to circumvent these limitatio
 ns\, exploiting a new experimental approach from the research group of Mat
 t Helgeson that yields microstructural information in complex flows of com
 plex fluids. The framework is constructed to automatically satisfy the key
  symmetry of microstructural evolution\, material frame indifference\, and
  enables data-driven determination of microstructural evolution equations 
 for complex fluids in very general flows.&nbsp\;
LOCATION:External
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