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Replication Data for: Measuring Complexity in Simulated Metal Microplasticity via Surrogate Modeling and Residual Analysis

Harvard Dataverse 2025 Score: 38 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Aaron E. Tallman Aaron E. Tallman

Summary

Researchers developed surrogate models using finite element simulation data from 100,000 uniaxial tension simulations to measure complexity in metal microplasticity, applying residual analysis to evaluate the performance of the models across local effective stress, strain, and cumulative plastic strain fields.

A compressed binary serialized file of python dataframe containing the extracted local effective stress, strain and plastic cumulative strain across 100,000 Finite element simulations of uniaxial tension.

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