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A machine-learning based model of non-Newtonian hydrodynamics with molecular fidelity

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SPL - New statistical physics in living matter: non equilibrium states under adaptive control

We introduce a machine-learning-based approach for constructing a continuum non-Newtonian fluid dynamics model directly from a      micro-scale description. To faithfully retain molecular fidelity, 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 conformation tensors can be derived from the micro-scale model and the relevant terms can be parametrized using machine learning. The final model, named the deep non-Newtonian model (DeePN2), takes the form of conventional non-Newtonian fluid dynamics models, with a new form of the objective tensor derivative. Numerical results demonstrate the accuracy of DeePN2.

This talk is part of the Isaac Newton Institute Seminar Series series.

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TBC, Cambridge Room C, Arts School Biochemistry Lecture Theatre, Sanger Building

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