2020/04/23 by Alma Rahat, Rahat, Alma, Michael Wood +1
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Neural and Evolutionary Computing (cs.NE) #Reservoir Engineering and Simulation Methods #Water resources management and optimization
paper · pdf · doi:10.48550/arxiv.2004.11055
openalex publication_date 2020/04/23 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
We are often interested in identifying the feasible subset of a decision\nspace under multiple constraints to permit effective design exploration. If\ndetermining feasibility required computationally expensive simulations, the\ncost of exploration would be prohibitive. Bayesian search is data-efficient for\nsuch problems: starting from a small dataset, the central concept is to use\nBayesian models of constraints with an acquisition function to locate promising\nsolutions that may improve predictions of feasibility when the dataset is\naugmented. At the end of this sequential active learning approach with a\nlimited number of expensive evaluations, the models can accurately predict the\nfeasibility of any solution obviating the need for full simulations. In this\npaper, we propose a novel acquisition function that combines the probability\nthat a solution lies at the boundary between feasible and infeasible spaces\n(representing exploitation) and the entropy in predictions (representing\nexploration). Experiments confirmed the efficacy of the proposed function.\n