About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces |
Author(s) |
Sterling G. Baird, Jason R. Hall, Ramsey Issa, Taylor D. Sparks |
On-Site Speaker (Planned) |
Sterling G. Baird |
Abstract Scope |
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and expertise. So, are the improvements in efficiency worth the cost of implementation? We demonstrate the compactness of a search space (to what extent and how degenerate solutions and non-solutions are removed) affects Bayesian optimization search efficiency. Here, we use the Adaptive Experimentation (Ax) Platform by Meta™ and a formulation optimization task with eight or nine tunable parameters, depending on search space compactness. In general, the removal of degeneracy through problem reformulation improves optimization efficiency. We recommend that optimization practitioners in the physical sciences carefully consider the trade-off between implementation cost and search efficiency before running expensive optimization campaigns. |
Proceedings Inclusion? |
Planned: |
Keywords |
Composites, Computational Materials Science & Engineering, Machine Learning |