DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network
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026105_1_5.0135015.pdf
Description
Published version
Size
8.7 MB
Format
Adobe PDF
Checksum (MD5)
fd295a81ba4ca49daffe5c3dc3f3c4e2
Author(s)
Hsu, Yu-Chuan
Buehler, Markus J
Date Issued
April 10, 2023
Journal
APL Machine Learning
Publisher
AIP Publishing
Citation
Yu-Chuan Hsu, Markus J. Buehler; DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network. APL Mach. Learn. 1 June 2023; 1 (2): 026105.
Version
Final published version
Abstract
The dynamics of material failure is a critical phenomenon relevant to a range of scientific and engineering fields, from healthcare to structural materials. We propose a specially designed deep neural network, DyFraNet, which can predict dynamic fracture behaviors by identifying a complete history of fracture propagation—from the onset of cracking, as a crack grows through the material, modeled as a series of frames evolving over time and dependent on each other. Furthermore, the model can not only forecast future fracture processes but also backcast to elucidate past fracture histories. In this scenario, once provided with the outcome of a fracture event, the model will reveal past events that led to this state and can also predict future evolutions of the failure process. By comparing the predicted results with atomistic-level simulations and theory, we show that DyFraNet can capture dynamic fracture mechanics by accurately predicting how cracks develop over time, including measures such as the crack speed, as well as when cracks become unstable. We use Gradient-weighted Class Activation Mapping, Grad-CAM, to interpret how DyFraNet perceives the relationship between geometric conditions and fracture dynamics, and we find that DyFraNet pays special attention to the areas around crack tips that have a critical influence in the early stage of fracture propagation. In later stages, the model pays increased attention to the existing or newly formed damaged regions in the material. The proposed approach offers the potential to accelerate the exploration of dynamical processes in material design against failure and can be adapted for all kinds of dynamical problems.
MIT Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics
Massachusetts Institute of Technology. Center for Computational Science and Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1063/5.0135015
