Predicting Material Strength Model Parameters Using a Machine Learning Based Approach
JP Allen, SA Andrews, KS Hickmann - Journal of Dynamic Behavior of …, 2024 - Springer
Abstract Machine learning (ML) is an emerging technology increasingly used to calibrate
material models. While reported results often demonstrate good fit to experimental set, there …
material models. While reported results often demonstrate good fit to experimental set, there …
Posterior Covariance Matrix Approximations
AC Schmid, SA Andrews - Journal of …, 2024 - asmedigitalcollection.asme.org
The Davis equation of state (EOS) is commonly used to model thermodynamic relationships
for high explosive (HE) reactants. Typically, the parameters in the EOS are calibrated, with …
for high explosive (HE) reactants. Typically, the parameters in the EOS are calibrated, with …
Parameter Calibration for Johnson Cook and Preston-Tonks-Wallace Material Strength Models with Uncertainty Quantification
AC Schmid, SA Andrews - 2022 - osti.gov
In this study we perform a Bayesian calibration of the parameters in the Johnson Cook (JC)
material strength model, with uncertainty, using experiments with for a range of low and …
material strength model, with uncertainty, using experiments with for a range of low and …