Dirty engineering data-driven inverse prediction machine learning model.

ABSTRACT: Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set of material conditions. In general, the input feature dimension (the number of material condition variables) is much higher than the output feature dimension (the number of material properties of concern). Rather than such a forward-prediction ML model, it is necessary to develop so-called inverse-design modeling, wherein required material conditions could be deduced from a set of desired material properties. Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse

SUBMITTER: Lee JW 

PROVIDER: S-EPMC7687896 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature