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RISPhysics-Informed Neural Network Engines to Predict Viscoelastic Behavior of Elastomers/Hydrogels
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. This study presents a physics-informed neural network (PINN) framework to model the nonlinear viscoelastic behavior of polymers and soft materials. By integrating principles from polymer science, statistical ...
A Data-Driven Model to Predict Constitutive and Failure Behavior of Elastomers Considering the Strain Rate, Temperature, and Filler Ratio
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: This new machine-learned (ML) constitutive model for elastomers has been developed to capture the dependence of elastomer behavior on loading conditions such as strain rate and temperature, as well as compound morphology ...