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Updated: Aug 28, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Data-driven optimal closures for mean-cluster models: Beyond the classical pair approximation
Avesta Ahmadi1, Jamie M Foster2, Bartosz Protas3
1School of Computational Science & Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4L8.
This study introduces a data-driven "sparse approximation" for modeling lattice dynamics, improving accuracy and interpretability over traditional methods. This new approach offers a solvable linear model for cluster concentrations in materials like Li-ion battery cathodes.
Area of Science:
- Materials Science
- Computational Chemistry
- Statistical Mechanics
Background:
- Lattice dynamics modeling often uses the mean-clustering approach, which generates infinite differential equations.
- Closure conditions are needed to approximate higher-order cluster concentrations, with pair approximation being common but problematic.
- Existing methods face challenges in accuracy and solving inverse problems for material parameters.
Purpose of the Study:
- To develop and validate a novel, data-driven closure condition for the mean-clustering approach.
- To introduce a generalized
Main Methods:
- Developed a data-driven strategy to calibrate an
Main Results:
- The proposed
Conclusions:
- The
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