| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Materials Design, Model Calibration, and Multi-fidelity Modeling with Latent Map Gaussian Processes |
| Author(s) |
Ramin Bostanabad |
| On-Site Speaker (Planned) |
Ramin Bostanabad |
| Abstract Scope |
I will introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs but are also applicable to mixed data that have both quantitative (e.g., pressure) and qualitative (e.g., coating type) inputs. I will elaborate on the core idea of LMGPs which consists of learning a low-dimensional latent space where all qualitative inputs are represented by some latent quantitative features. Through a wide range of analytical and real-world examples, I will demonstrate the advantages of LMGPs in terms of accuracy and versatility. I will show that LMGPs (1) can handle variable-length inputs, (2) have a nice neural network interpretation, (3) dispense with manual featurization in Bayesian optimization. I will also demonstrate that LMGPs can fuse multiple sources of information together without imposing any hard constraints on how information sources, regardless of their fidelity level, should be fused or how the covariance of the errors is structured. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation |