A Data-Driven Computational Workflow For The Growth Of Porous Anodized Aluminum Oxide: Integration Of Surrogate Modeling And Uncertainty Quantification - Albaraghteh et al 2025a - Scipedia

Data-driven Mechanics and Physics of Materials

T. Albaraghteh, C. Feiler, M. Zheludkevich, D. Höche

Full Paper

Back to Top

Document information

Published on 06/07/25
Submitted on 06/07/25

Volume Probabilistic Machine Learning, 2025
DOI: 10.23967/data-driven.2025.019
Licence: CC BY-NC-SA license

Share this document

claim authorship

Are you one of the authors of this document?