  Fluid interactions permeate daily human activities, with properties like
density and viscosity playing pivotal roles in household tasks. While density
estimation is straightforward through Archimedes' principle, viscosity poses a
more intricate challenge, especially given the varied behaviors of Newtonian
and non-Newtonian fluids. These fluids, which differ in their stress-strain
relationships, are delineated by specific constitutive models such as the
Carreau, Cross, and Herschel-Bulkley models, each possessing unique viscosity
parameters. This study introduces a novel differentiable fitting framework,
DiffStir, tailored to identify key physics parameters via the common daily
operation of stirring. By employing a robotic arm for stirring and harnessing a
differentiable Material Point Method (diffMPM)-based simulator, the framework
can determine fluid parameters by matching observations from both the simulator
and the real world. Recognizing the distinct preferences of the aforementioned
constitutive models for specific fluids, an online strategy was adopted to
adaptively select the most fitting model based on real-world data.
Additionally, we propose a refining neural network to bridge the sim-to-real
gap and mitigate sensor noise-induced inaccuracies. Comprehensive experiments
were conducted to validate the efficacy of DiffStir, showcasing its precision
in parameter estimation when benchmarked against reported literature values.
More experiments and videos can be found in the supplementary materials and on
the website: https://sites.google.com/view/diffstir.
