(Richard Sheridan, Cate Brinson)
In this module, students will practice optimizing materials. These materials will be represented in a geometric basis, which allows a flexible set of materials to be derived from combinations of basis elements. Students will then use a simulation model to determine the performance of these materials. Leveraging these tools, students will subsequently optimize the material’s performance using Bayesian optimization on the ML model. Bayesian optimization allows uncertainty calculations to be used for optimization: If there is a high-performing material whose uncertainty in performance is high, the Bayesian optimization routine might suggest to then calculate the performance using the simulation model in order to determine its performance with more certainty, as this material formulation could be close to the optima. Over iterations, the Bayesian optimization routine will come closer and closer to the optimal material. Running the simulation repeatedly is expensive, so students will come to understand the value of a careful exploration-exploitation tradeoff.
1. Students will grasp the fundamentals of Bayesian optimization and the exploration-exploitation tradeoff as indicated by their ability to:
- accurately describe differences and similarities between BO and other optimization/response surface methods,
- give examples of what “decisions” BO algorithms would make given example data,
- give an example optimization problem, make and explain their own choice between explorative vs exploitative decision policies.
2. Students will apply Bayesian optimization to (approximately) find a globally optimal material parameter set.