8 citations · 10 across the 6 of their papers we have counts for
3 papers · 1 filter
Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction
Simon Gonzalez-Zapata, Aidan Pantoya, Chunyu Li +2
The shock-to-detonation transition in energetic materials is governed by coupled processes spanning Angstroms to millimeters and femtoseconds to microseconds, where traditional mul…
Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net
Brian H. Lee, Chunyu Li, Aidan Pantoya +3
Mapping microstructure to properties is central to materials science. Perhaps most famously, the Hall-Petch relationship relates average grain size to strength. More challenging ha…
Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
George D. Pasparakis, Himanshu Sharma, Rushik Desai +4
A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using da…