|About this Abstract
|2022 TMS Annual Meeting & Exhibition
|AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|Accelerated Alloy Design by Batch Constrained Multiobjective Optimization Using Surrogate Models
|Gérard Ramstein, Franck Tancret
|On-Site Speaker (Planned)
Recent alloy design strategies use constrained multiobjective optimization by genetic algorithms. However, due to the high number of evaluations they require, they can rely only on fast predictive tools like machine learning or parsimonious results from the calculation of phase diagrams (Calphad / Thermo-Calc). The present work addresses multiobjective optimization when objectives and/or constraints require computer-intensive calculations (e.g. solidification, diffusion, precipitation, phase field, ab initio simulation…). It is inspired from batch Bayesian optimization; it uses an evolutionary optimizer relying on the predictions of Kriging surrogate models (Gaussian processes), and a selector of the best solutions to be evaluated in sequential batches to improve both the global Pareto-optimal solutions and the surrogate models accuracy. The algorithm is exemplified and benchmarked in the case of the design of nickel-based superalloys. Massive reductions in computing times are achieved, with negligible losses on the attainment of the Pareto front.
|Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation