Publications

Support Vector Machines for Estimating Decision Boundaries with Numerical Simulations

Walsh, Timothy W.; Aquino, Wilkins A.; Kurzawski, Andrew K.; McCormick, Cameron M.; Sanders, Clay M.; Smith, Chandler B.; Treweek, Benjamin T.

Many engineering design problems can be formulated as decisions between two possible options. This is the case, for example, when a quantity of interest must be maintained below or above some threshold. The threshold thereby determines which input parameters lead to which option, and creates a boundary between the two options known as the decision boundary. This report details a machine learning approach for estimating decision boundaries, based on support vector machines (SVMs), that is amenable to large scale computational simulations. Because it is computationally expensive to evaluate each training sample, the approach iteratively estimates the decision boundary in a manner that requires relatively few training samples to glean useful estimates. The approach is then demonstrated on three example problems from structural mechanics and heat transport.