Seyed Mohammad Taghavii
August 8, 2025
Scientific Machine Learning for Complex Fluid Flows
Recent advances in machine learning have significantly influenced the simulation of Newtonian fluids, and there is growing interest in extending these techniques to complex fluids with non-Newtonian properties, such as viscoelastic flows. In this talk, we present data-driven frameworks for constructing interpretable reduced-order models of viscoelastic fluids, combining both linear and nonlinear dimensionality reduction methods with the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm. In particular, we explore the development of physics-informed metrics to enhance nonlinear dimensionality reduction tailored to viscoelastic behavior. We also highlight recent progress in the SciML domain, with a focus on applications to droplet dynamics.
