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Stress-strain curves, or more generally, stress functions, are an extremelyimportant characterization of a material's mechanical properties. However,stress functions are often difficult to derive and are narrowly tailored to aspecific material. Further, large deformations, high strain-rates, temperaturesensitivity, and effect of material parameters compound modeling challenges. Wepropose a generalized deep neural network approach to model stress as a statefunction with quantile regression to capture uncertainty. We extend thesemodels to uniaxial impact mechanics using stochastic differential equations todemonstrate a use case and provide a framework for implementing thisuncertainty-aware stress function. We provide experiments benchmarking ourapproach against leading constitutive, machine learning, and transfer learningapproaches to stress and impact mechanics modeling on publicly available andnewly presented data sets. We also provide a framework to optimize materialparameters given multiple competing impact scenarios.

Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics