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Quantifying Topological Uncertainty in Fractured Systems using Graph Theory and Machine Learning.
Gowri Srinivasan1, Jeffrey D Hyman2, David A Osthus3
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. gowri@lanl.gov.
Scientific Reports
|August 5, 2018
Summary
This study introduces a novel method combining computational physics, machine learning, and graph theory to model fractured systems. The approach efficiently captures micro-fracture details, significantly improving prediction accuracy and speed for materials science and engineering applications.
Area of Science:
- Materials Science
- Computational Physics
- Applied Mathematics
Background:
- Fractured systems are common in engineering and natural phenomena, like hydraulic fracturing and material failure.
- Microstructural details are crucial for understanding these systems but are difficult to model at larger scales.
- Existing models often oversimplify or ignore microscale information due to computational limitations.
Purpose of the Study:
- To develop a new framework for efficiently modeling fractured systems.
- To bridge the gap between high-fidelity microscale models and computationally feasible macroscale predictions.
- To integrate computational physics, machine learning, and graph theory for enhanced predictive capabilities.
Main Methods:
- Utilizing graph theory to represent discrete fracture networks.
- Employing machine learning to accelerate model computations.
- Developing a method to transition from high-fidelity models to coarse-scale graph representations.
Main Results:
- The proposed method significantly reduces the degrees of freedom needed to represent micro-fracture information.
- Compact graph representations effectively capture critical structural details.
- Machine learning integration leads to substantial acceleration of predictive models.
Conclusions:
- This approach offers a paradigm shift in modeling fractured systems, moving beyond computationally intensive methods.
- The integration of diverse techniques enhances prediction accuracy and achieves up to four orders of magnitude speedup.
- The method provides a computationally efficient way to utilize microstructural information for macroscale behavior prediction.