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High-fidelity complex engineering simulations are highly predictive, but alsocomputationally expensive and often require substantial computational efforts.The mitigation of computational burden is usually enabled through parallelismin high-performance cluster (HPC) architecture. In this paper, an asynchronousconstrained batch-parallel Bayesian optimization method is proposed toefficiently solve the computationally-expensive simulation-based optimizationproblems on the HPC platform, with a budgeted computational resource, where themaximum number of simulations is a constant. The advantages of this method arethree-fold. First, the efficiency of the Bayesian optimization is improved,where multiple input locations are evaluated massively parallel in anasynchronous manner to accelerate the optimization convergence with respect tophysical runtime. This efficiency feature is further improved so that when eachof the inputs is finished, another input is queried without waiting for thewhole batch to complete. Second, the method can handle both known and unknownconstraints. Third, the proposed method considers several acquisition functionsat the same time and sample based on an evolving probability mass distributionfunction using a modified GP-Hedge scheme, where parameters are correspondingto the performance of each acquisition function. The proposed framework istermed aphBO-2GP-3B, which corresponds to asynchronous parallel hedge Bayesianoptimization with two Gaussian processes and three batches. The aphBO-2GP-3Bframework is demonstrated using two high-fidelity expensive industrialapplications, where the first one is based on finite element analysis (FEA) andthe second one is based on computational fluid dynamics (CFD) simulations.

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition  functions for constrained Bayesian optimization on high-performing computing  architecture