Abstract:

Advances in modeling and computation have resulted in high-fidelity digital models capable of simulating the dynamics of a wide range of industrial systems. These models often require calibration, or the estimation of an optimal set of parameters, to reflect a system’s observed behavior. While searching over the parameter space is an inevitable part of the calibration process, models are seldom designed to be valid for arbitrarily large parameter spaces. Application of existing black-box calibration methods, therefore, often require repeatedly evaluating a model over a wide range of parameters. For some parameter combinations, the simulations could be unreasonably slow or fail altogether. In general, the shape of subregions in the parameter space that could result in simulation failure is unknown and near-impossible to ascertain analytically. In this paper, we propose a novel failure robust Bayesian optimization (FR-BO) algorithm that learns these failure regions from simulation data and informs a Bayesian optimization algorithm to avoid failure regions while searching for optimal parameters. This results in acceleration of the optimizer’s convergence and prevents wastage of time trying to simulate parameters with high failure probabilities. Index Terms—Machine learning; Bayesian optimization; model simulation; digital twin; feasibility analysis; numerical methods; Gaussian processes.