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Using Gaussian Process Regression (GPR) models with the Matérn covariance function to predict the dynamic viscosity and torque of SiO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1345" altimg="si4.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mi mathvariant="bold">2</mml:mi></mml:mrow></mml:msub></mml:math>/Ethylene glycol nanofluid: A machine learning approach

Gaussian Process RegressionRheological MeasurementFluid PropertiesEngineeringMultiscale MechanicsDynamic ViscosityHydrodynamic LubricationFluid MechanicsMechanical EngineeringRheologyMaterial MechanicsTribological PropertyMatérn Covariance FunctionMechanics Modeling