Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications
| Autor: | 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 |
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| Rok vydání: | 2019 |
| Předmět: |
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 |
| Zdroj: | Computational Materials Science. 157:87-98 |
| ISSN: | 0927-0256 |
| DOI: | 10.1016/j.commatsci.2018.10.036 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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