Data-Centric Mixed-Variable Bayesian Optimization For Materials Design
Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify "lack of data" uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationNavigateMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A comparison of mixed-variables Bayesian optimization approaches
Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a numerically c…
Bayesian OptimizationGaussian ProcessesHybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed n…
Bayesian OptimizationGaussian ProcessesModel SelectionPositionMixed-Variable Bayesian Optimization
The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian op…
Bayesian OptimizationThompson SamplingBayesian Optimization For Multi-Objective Mixed-Variable Problems
Optimizing multiple, non-preferential objectives for mixed-variable, expensive black-box problems is important in many areas of engineering and science. The expensive, noisy, black-box nature of these problems makes them…
Bayesian OptimizationHybrid Reinforcement Learning Framework for Mixed-Variable Problems
Optimization problems characterized by both discrete and continuous variables are common across various disciplines, presenting unique challenges due to their complex solution landscapes and the difficulty of navigating …
Bayesian Optimizationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1