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SUMMARY:A machine-learning based model of non-Newtonian hydrodynamics with
  molecular fidelity - Huan Lei (Michigan State University)
DTSTART:20231211T150000Z
DTEND:20231211T160000Z
UID:TALK208942@talks.cam.ac.uk
DESCRIPTION:We introduce a machine-learning-based approach for constructin
 g a continuum non-Newtonian fluid dynamics model directly from a &nbsp\; &
 nbsp\; &nbsp\;micro-scale description. To faithfully retain molecular fide
 lity\, we establish a micro-macro correspondence via a set of encoders for
  the micro-scale polymer configurations and their macro-scale counterparts
 \, a set of nonlinear conformation tensors. The dynamics of these conforma
 tion tensors can be derived from the micro-scale model and the relevant te
 rms can be parametrized using machine learning. The final model\, named th
 e deep non-Newtonian model (DeePN2)\, takes the form of conventional non-N
 ewtonian fluid dynamics models\, with a new form of the objective tensor d
 erivative. Numerical results demonstrate the accuracy of DeePN2.
LOCATION:Seminar Room 2\, Newton Institute
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