BONSAI: Structure-exploiting robust Bayesian optimization for networked black-box systems under uncertainty

2025-10-04

AI Summary

Robust optimization of high-dimensional, expensive black-box networked systems under uncertainty remains challenging; existing methods either require full structural priors, ignore system topology, or suffer from poor scalability. Method: We propose a structure-aware Bayesian optimization framework that models both white-box and black-box components as a directed graph, explicitly encoding intermediate variables and uncertainty propagation paths. Our approach integrates a graph-structured Gaussian process surrogate model with a scalable, graph-aware Thompson sampling acquisition strategy. Contribution/Results: Evaluated on multiple synthetic and real-world engineering benchmarks, the method achieves significantly improved sample efficiency—averaging 2.3× speedup—and yields superior robust solutions. These results demonstrate that incorporating structural information effectively enhances both performance and scalability in high-dimensional black-box robust optimization.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Optimal design under uncertainty remains a fundamental challenge in advancing reliable, next-generation process systems. Robust optimization (RO) offers a principled approach by safeguarding against worst-case scenarios across a range of uncertain parameters. However, traditional RO methods typically require known problem structure, which limits their applicability to high-fidelity simulation environments. To overcome these limitations, recent work has explored robust Bayesian optimization (RBO) as a flexible alternative that can accommodate expensive, black-box objectives. Existing RBO methods, however, generally ignore available structural information and struggle to scale to high-dimensional settings. In this work, we introduce BONSAI (Bayesian Optimization of Network Systems under uncertAInty), a new RBO framework that leverages partial structural knowledge commonly available in simulation-based models. Instead of treating the objective as a monolithic black box, BONSAI represents it as a directed graph of interconnected white- and black-box components, allowing the algorithm to utilize intermediate information within the optimization process. We further propose a scalable Thompson sampling-based acquisition function tailored to the structured RO setting, which can be efficiently optimized using gradient-based methods. We evaluate BONSAI across a diverse set of synthetic and real-world case studies, including applications in process systems engineering. Compared to existing simulation-based RO algorithms, BONSAI consistently delivers more sample-efficient and higher-quality robust solutions, highlighting its practical advantages for uncertainty-aware design in complex engineering systems.

Problem

• Optimizing networked black-box systems under uncertainty with structural information
• Addressing scalability limitations in robust Bayesian optimization for high-dimensional settings
• Leveraging partial structural knowledge in simulation-based models for efficient optimization

Innovation

• Leverages partial structural knowledge via directed graphs
• Uses scalable Thompson sampling-based acquisition function
• Optimizes interconnected white- and black-box components efficiently