95 citations · 414 across the 15 of their papers we have counts for
15 papers
Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration
Aleksandra Pachalieva, Jeffrey D. Hyman, Daniel O'Malley +2
We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO mineralization req…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Yinan Wang, Diane Oyen, Weihong +7
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal…
Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design
M. Giselle Fernández-Godino, Michael J. Grosskopf, Julia B. Nakhleh +3
A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current…
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning
Julia B. Nakhleh, M. Giselle Fernández-Godino, Michael J. Grosskopf +3
Building a sustainable burn platform in inertial confinement fusion (ICF) requires an understanding of the complex coupling of physical processes and the effects that key experimen…
Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations with Machine Learning
M. Giselle Fernández-Godino, Nishant Panda, Daniel O'Malley +4
Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is com…