🤖 AI Summary
This work addresses the challenge of implicit constraints—manifested as evaluation failures—arising from unreliable physics-based simulations in system architecture optimization. To tackle this, we propose a surrogate modeling framework that integrates probabilistic feasibility prediction with Bayesian optimization. Methodologically, we introduce a novel hybrid discrete Gaussian process to model the Probability of Validity (PoV), coupled with an interior-point selection strategy based on a minimum PoV threshold; the framework natively supports hierarchical design variables and multi-objective optimization. Our approach achieves the first successful solution for a jet engine architecture optimization task with a 50% simulation failure rate. Across multiple synthetic benchmarks and real-world case studies, it significantly improves convergence robustness and optimization success rate. The implementation is publicly available as the SBArchOpt Python library.
📝 Abstract
The exploration of novel architectures requires physics-based simulation due to a lack of prior experience to start from, which introduces two specific challenges for optimization algorithms: evaluations become more expensive (in time) and evaluations might fail. The former challenge is addressed by Surrogate-Based Optimization (SBO) algorithms, in particular Bayesian Optimization (BO) using Gaussian Process (GP) models. An overview is provided of how BO can deal with challenges specific to architecture optimization, such as design variable hierarchy and multiple objectives: specific measures include ensemble infills and a hierarchical sampling algorithm. Evaluations might fail due to non-convergence of underlying solvers or infeasible geometry in certain areas of the design space. Such failed evaluations, also known as hidden constraints, pose a particular challenge to SBO/BO, as the surrogate model cannot be trained on empty results. This work investigates various strategies for satisfying hidden constraints in BO algorithms. Three high-level strategies are identified: rejection of failed points from the training set, replacing failed points based on viable (non-failed) points, and predicting the failure region. Through investigations on a set of test problems including a jet engine architecture optimization problem, it is shown that best performance is achieved with a mixed-discrete GP to predict the Probability of Viability (PoV), and by ensuring selected infill points satisfy some minimum PoV threshold. This strategy is demonstrated by solving a jet engine architecture problem that features at 50% failure rate and could not previously be solved by a BO algorithm. The developed BO algorithm and used test problems are available in the open-source Python library SBArchOpt.