Theory aware Machine Learning (TaML)
type: Dataset
description: A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data. For additional details on the code, please refer to the README.md provided with the code repository (GitHub Repo for Theory aware Machine Learning). For additional details on the methodology, see a forthcoming manuscript titled "Leveraging theory for enhanced machine learning," by Debra J. Audus, Austin McDannald and Brian DeCost.
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: geo
keyword: geoss
keyword: machine-learning
keyword: national
keyword: north-america
keyword: polymers
keyword: theory
keyword: transfer-learning
keyword: united-states
issued: 2025-11-21T18:34:38.616247
modified: 2022-05-06T00:00:00
language: {en}
theme: Uncertainty quantification"
theme: Modeling and computational material science"
theme: Data and informatics"
theme: Polymers}
contact point: Organization
  fn: Debra Audus

US Migrating
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README
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GitHub Repo for Theory aware Machine Learning (TaML)
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