Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Journal Article · Mon Jul 20 00:00:00 EDT 2020 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2020.109719· OSTI ID:1645087

Panda, Nishant ^[1]; Osthus, David Allen ^[1]; Srinivasan, Gowri ^[1]; O'Malley, Daniel ^[1]; Chau, Viet Tuan ^[1]; Oyen, Diane Adele ^[1]; Godinez Vazquez, Humberto C. ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: LDRD; USDOE; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1645087
• Alternate ID(s): OSTI ID: 1809281
• Report Number(s): LA-UR--19-30618
• Journal Information: Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 420; ISSN 0021-9991
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
97 MATHEMATICS AND COMPUTING
Uncertainty quantification
data driven upscaling
fracture propagation
machine learning
probabilistic emulator
reduced order model