Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation

@article{Sun2021DatadrivenDO,
  title={Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation},
  author={Xiao Sun and Bahador Bahmani and Nikolaos N. Vlassis and Waiching Sun and Yanxun Xu},
  journal={ArXiv},
  year={2021},
  volume={abs/2105.09980}
}
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ). We first develop a causal discovery algorithm to infer causal relations among time-history data measured during each representative volume element (RVE) simulation through a directed acyclic graph (DAG). With multiple plausible sets of causal relationships estimated from multiple RVE simulations, the predictions are propagated in the derived causal graph while… 
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