Data-driven low-dimensional model of a sedimenting flexible fiber

arXiv:2405.10442v1 Announce Type: cross
Abstract: The dynamics of flexible filaments entrained in flow, important for understanding many biological and industrial processes, are computationally expensive to model with full-physics simulations. This work describes a data-driven technique to create high-fidelity low-dimensional models of flexible fiber dynamics using machine learning; the technique is applied to sedimentation in a quiescent, viscous Newtonian fluid, using results from detailed simulations as the data set. The approach combines an autoencoder neural network architecture to learn a low-dimensional …

abstract arxiv create cs.lg data data-driven dynamics fidelity flow industrial low machine machine learning physics physics.flu-dyn processes simulations type understanding work