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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
- 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.
- Subjects :
- Feature engineering
Length scale
General Computer Science
Scale (ratio)
General Physics and Astronomy
02 engineering and technology
010402 general chemistry
Machine learning
computer.software_genre
01 natural sciences
Reduced order
General Materials Science
Physical model
business.industry
Fracture mechanics
General Chemistry
021001 nanoscience & nanotechnology
0104 chemical sciences
Computational Mathematics
Mechanics of Materials
Key (cryptography)
Artificial intelligence
0210 nano-technology
business
computer
Brittle fracture
Subjects
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