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

Abstract

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 damagemore » and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1645087
Alternate Identifier(s):
OSTI ID: 1809281
Report Number(s):
LA-UR-19-30618
Journal ID: ISSN 0021-9991
Grant/Contract Number:  
89233218CNA000001; 20170103DR
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Volume: 420; Journal ID: ISSN 0021-9991
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; Uncertainty quantification; reduced order model; machine learning; data driven upscaling; probabilistic emulator; fracture propagation

Citation Formats

Panda, Nishant, Osthus, David Allen, Srinivasan, Gowri, O'Malley, Daniel, Chau, Viet Tuan, Oyen, Diane Adele, and Godinez Vazquez, Humberto C. Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. United States: N. p., 2020. Web. https://doi.org/10.1016/j.jcp.2020.109719.
Panda, Nishant, Osthus, David Allen, Srinivasan, Gowri, O'Malley, Daniel, Chau, Viet Tuan, Oyen, Diane Adele, & Godinez Vazquez, Humberto C. Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling. United States. https://doi.org/10.1016/j.jcp.2020.109719
Panda, Nishant, Osthus, David Allen, Srinivasan, Gowri, O'Malley, Daniel, Chau, Viet Tuan, Oyen, Diane Adele, and Godinez Vazquez, Humberto C. Mon . "Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling". United States. https://doi.org/10.1016/j.jcp.2020.109719. https://www.osti.gov/servlets/purl/1645087.
@article{osti_1645087,
title = {Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling},
author = {Panda, Nishant and Osthus, David Allen and Srinivasan, Gowri and O'Malley, Daniel and Chau, Viet Tuan and Oyen, Diane Adele and Godinez Vazquez, Humberto C.},
abstractNote = {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.},
doi = {10.1016/j.jcp.2020.109719},
journal = {Journal of Computational Physics},
number = ,
volume = 420,
place = {United States},
year = {2020},
month = {7}
}

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