Abstract
We present a comprehensive benchmarking framework for evaluating machine-learning approaches applied to phase-field problems. This framework focuses on four key analysis areas crucial for assessing the performance of such approaches in a systematic and structured way. Firstly, interpolation tasks are examined to identify trends in prediction accuracy and accumulation of error over simulation time. Secondly, extrapolation tasks are also evaluated according to the same metrics. Thirdly, the relationship between model performance and data requirements is investigated to understand the impact on predictions and robustness of these approaches. Finally, systematic errors are analyzed to identify specific events or inadvertent rare events triggering high errors. Quantitative metrics evaluating the local and global description of the microstructure evolution, along with other scalar metrics representative of phase-field problems, are used across these four analysis areas. This benchmarking framework provides a path to evaluate the effectiveness and limitations of machine-learning strategies applied to phase-field problems, ultimately facilitating their practical application.
| Original language | English (US) |
|---|---|
| Article number | 065019 |
| Journal | Modelling and Simulation in Materials Science and Engineering |
| Volume | 32 |
| Issue number | 6 |
| DOIs | |
| State | Published - Sep 2024 |
Funding
The authors would like to thank Daniel Vizoso for his insightful feedback and suggestions. PWV acknowledges the financial assistance Award 70NANB14H012 from the U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). VA acknowledges Terra supercomputing facility at Texas A&M University for providing essential computational resources that facilitated the research presented in this paper. Machine-learning capabilities and computational resources are supported in part by the Center for Integrated Nanotechnologies, an Office of Science user facility operated for the U.S. Department of Energy. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title, and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan www.energy.gov/downloads/doe-public-access-plan .
Keywords
- benchmark
- machine learning
- phase field
ASJC Scopus subject areas
- Modeling and Simulation
- General Materials Science
- Condensed Matter Physics
- Mechanics of Materials
- Computer Science Applications