activity
20182020
most citedStressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials

8 citations · 8 across the 2 of their papers we have counts for

collaborators

7 papers

cs.LG20208 cited

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…

physics.plasm-ph2020

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…

cond-mat.mtrl-sci2020

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…

physics.comp-ph2019

Multilevel Graph Partitioning for Three-Dimensional Discrete Fracture Network Flow Simulations

Hayato Ushijima-Mwesigwa, Jeffrey D. Hyman, Aric Hagberg +5

We present a topology-based method for mesh-partitioning in three-dimensional discrete fracture network (DFN) simulations that take advantage of the intrinsic multi-level nature of…

physics.geo-ph2018

Branching of Hydraulic Cracks in Gas or Oil Shale with Closed Natural Fractures: How to Master Permeability

Saeed Rahimi-Agham, Viet-Tuan Chau, Huynjin Lee +7

While the hydraulic fracturing technology, aka fracking (or fraccing, frac), has become highly developed and astonishingly successful, a consistent formulation of the associated fr…

cond-mat.mtrl-sci2018

Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks

Max Schwarzer, Bryce Rogan, Yadong Ruan +8

We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these material…