**About this Abstract** |

**Meeting** |
**2022 TMS Annual Meeting & Exhibition
** |

**Symposium
** |
**AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
** |

**Presentation Title** |
Efficient Generation of Arbitrary N-field Microstructures from 2-point Statistics Using Multioutput Gaussian Random Fields |

**Author(s)** |
Andreas E. Robertson, Surya Kalidindi |

**On-Site Speaker (Planned)** |
Andreas E. Robertson |

**Abstract Scope** |
The ability to efficiently generate microstructure instances corresponding to specified statistical descriptors (two-point statistics) is a crucial component in rigorously studying random heterogeneous materials within the Integrated Computational Materials Engineering and Materials Informatics frameworks. However, the lack of computationally efficient, statistically expressive models for achieving this transformation has severely limited researchers’ ability to construct statistically diverse data-sets for these studies. In this presentation, we present a theoretical and computational framework for generating microstructural instances corresponding to specified two-point statistics by stochastically modeling the microstructure as an N-output Gaussian Random Field. Specifically, we illustrate how two-point statistics can be used to parameterize statistically anisotropic Gaussian Random Fields and we propose the algorithms necessary to efficiently sample these fields (under 0.5 seconds per sample for N phases). Additionally, we address the usefulness of this framework to the future of Materials Informatics. |

**Proceedings Inclusion?** |
Planned: |

**Keywords** |
Other, Other, Other |