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

Differentiable Fluid Physics Parameter Identification Via Stirring