About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Adaptive Learning from Scarce and Multi-Fidelity Data |
Author(s) |
Amin Yousefpour, Mehdi Shishehbor, Zahra Zanjani Foumani, Ramin Bostanabad |
On-Site Speaker (Planned) |
Amin Yousefpour |
Abstract Scope |
Materials modeling and design are typically hampered by two major uncertainty sources: lack of data and model form discrepancies. In this talk, we will present a novel approach based on nonlinear manifold learning that addresses these (and more) uncertainty sources. Our approach is based on latent map Gaussian processes (LMGPs) and aims to leverage multiple data sources to quantify uncertainty sources and, more importantly, provide visually interpretable diagnostic measures that indicate the extent to which different data sources (e.g., experiments, simulations, etc.) agree with one another. We will demonstrate that our approach performs well in a wide range of applications where there may be missing data, calibration parameters, or source-dependent noise. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Other |