Publicly Offered Research
Grant-in-Aid for Transformative Research Areas (A)
We will improve and optimize the learning methods we have developed for (A) multi-scale simulations of polymer flows and (B) the inference of Stokes flows with missing and/or noisy data. For the former, we will learn the constitutive relation for the canonical polymer entanglement model (i.e., Doi-Takimoto), and use it to simulate the dynamics of entangled polymer melt flows in 2D/3D. For the latter, we will incorporate hydrodynamics stresses and moving boundaries into the inference framework, to consider experimentally relevant flow problems (e.g., biofluids and colloidal dispersions).
