A data-driven method for automated data superposition with applications in soft matter science
Name
a-data-driven-method-for-automated-data-superposition-with-applications-in-soft-matter-science.pdf
Size
1.66 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Lennon, Kyle R.
McKinley, Gareth H.
Swan, James W.
Date Issued
May 25, 2023
Journal
Data-Centric Engineering
Publisher
Cambridge University Press (CUP)
Citation
Lennon KR, McKinley GH, Swan JW. A data-driven method for automated data superposition with applications in soft matter science. Data-Centric Engineering. 2023;4:e13.
Version
Final published version
Abstract
The superposition of data sets with internal parametric self-similarity is a longstanding and widespread technique for the analysis of many types of experimental data across the physical sciences. Typically, this superposition is performed manually, or recently through the application of one of a few automated algorithms. However, these methods are often heuristic in nature, are prone to user bias via manual data shifting or parameterization, and lack a native framework for handling uncertainty in both the data and the resulting model of the superposed data. In this work, we develop a data-driven, nonparametric method for superposing experimental data with arbitrary coordinate transformations, which employs Gaussian process regression to learn statistical models that describe the data, and then uses maximum a posteriori estimation to optimally superpose the data sets. This statistical framework is robust to experimental noise and automatically produces uncertainty estimates for the learned coordinate transformations. Moreover, it is distinguished from black-box machine learning in its interpretability—specifically, it produces a model that may itself be interrogated to gain insight into the system under study. We demonstrate these salient features of our method through its application to four representative data sets characterizing the mechanics of soft materials. In every case, our method replicates results obtained using other approaches, but with reduced bias and the addition of uncertainty estimates. This method enables a standardized, statistical treatment of self-similar data across many fields, producing interpretable data-driven models that may inform applications such as materials classification, design, and discovery.
Subjects
Applied Mathematics
Computer Science Applications
General Engineering
Statistics and Probability
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1017/dce.2023.3
