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Workshop: Machine Learning for Engineering Modeling, Simulation and Design

Constraint active search for experimental design

Gustavo Malkomes · Harvey Cheng · Michael McCourt


Many problems in engineering and design require balancing competing objectives under the presence of uncertainty. The standard approach in the literature characterizes the relationship between design decisions and their corresponding outcomes as a Pareto frontier, which is discovered through multiobjective optimization. In this position paper, we suggest that this approach is not ideal for reasoning about practical design decisions. Instead of multiobjective optimization, we propose soliciting desired minimum performance constraints on all objectives to define regions of satisfactory. We present work-in-progress which visualizes the design decisions that consistently satisfy user-defined thresholds in an additive manufacturing problem.

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