Data-Driven Discovery of Polymer Shape Dynamics in Hydrodynamic Environments

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Abstract

This work applies data-driven and deep-learning methods to characterize the shape dynamics of polymer chains under hydrodynamic flow. Using bead-spring simulations of von Willebrand Factor (vWF) conformations combined with learned low-dimensional representations, the study reveals the coil-to-stretch transition dynamics of the polymer in a compact latent embedding space.

BibTeX

@inproceedings{upreti2025datadriven,
  title     = {Data-Driven Discovery of Polymer Shape Dynamics in Hydrodynamic Environments},
  author    = {Upreti, S. and Chengalrayan, S. and Usta, M.},
  booktitle = {17th U.S. National Congress on Computational Mechanics (USNCCM17)},
  year      = {2025},
  note      = {Poster presentation}
}