3 research outputs found
Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics
Stress-strain curves, or more generally, stress functions, are an extremely
important characterization of a material's mechanical properties. However,
stress functions are often difficult to derive and are narrowly tailored to a
specific material. Further, large deformations, high strain-rates, temperature
sensitivity, and effect of material parameters compound modeling challenges. We
propose a generalized deep neural network approach to model stress as a state
function with quantile regression to capture uncertainty. We extend these
models to uniaxial impact mechanics using stochastic differential equations to
demonstrate a use case and provide a framework for implementing this
uncertainty-aware stress function. We provide experiments benchmarking our
approach against leading constitutive, machine learning, and transfer learning
approaches to stress and impact mechanics modeling on publicly available and
newly presented data sets. We also provide a framework to optimize material
parameters given multiple competing impact scenarios.Comment: Index Terms: Stress, Uncertainty, Impact Mechanics, Deep Learning,
Neural Network. 10 pages, 9 figures, 6 table