Back to Search Start Over

Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications

Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications

Authors :
Satish Karra
Maruti Kumar Mudunuru
Daniel O'Malley
Bryan A. Moore
Roselyne Tchoua
Gowri Srinivasan
Esteban Rougier
Chandramouli Nyshadham
Viet T. Chau
Abigail Hunter
Hari S. Viswanathan
Source :
Computational Materials Science. 157:87-98
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

Abstract

Typically, thousands of computationally expensive micro-scale simulations of brittle crack propagation are needed to upscale lower length scale phenomena to the macro-continuum scale. Running such a large number of crack propagation simulations presents a significant computational challenge, making reduced-order models (ROMs) attractive for this task. The ultimate goal of this research is to develop ROMs that have sufficient accuracy and low computational cost so that these upscaling simulations can be readily performed. However, constructing ROMs for these complex simulations presents its own challenge. Here, we present and compare four different approaches for reduced-order modeling of brittle crack propagation in geomaterials. These methods rely on machine learning (ML) and graph-theoretic algorithms to approximate key aspects of the brittle crack problem. These methods also incorporate different physics-based assumptions in order to reduce the training requirements while maintaining accurate physics as much as possible. Results from the ROMs are directly compared against a high-fidelity model of brittle crack propagation. Further, the strengths and weaknesses of the ROMs are discussed, and we conclude that combining smart physics-informed feature engineering with highly trainable ML models provides the best performance. The ROMs considered here have computational costs that are orders-of-magnitude less than the cost associated with high-fidelity physical models while maintaining good accuracy.

Details

ISSN :
09270256
Volume :
157
Database :
OpenAIRE
Journal :
Computational Materials Science
Accession number :
edsair.doi...........ab95dd51efbd6914b82c09304f8b1e0d
Full Text :
https://doi.org/10.1016/j.commatsci.2018.10.036