Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments

2026-02-11

AI Summary

This work addresses the challenge of automatic alignment in complex scientific instruments, where high-dimensional, strongly coupled parameters and a highly asymmetric objective function severely degrade the performance of traditional Bayesian optimization (BO). To overcome this, the authors propose a physics-informed coordinate transformation that decouples the parameters and aligns the active subspace with the primary search axes, thereby reformulating the high-dimensional problem into a low-dimensional effective subspace. By integrating this transformation with reverse annealing and trust-region Bayesian optimization (TuRBO), the method achieves significantly enhanced global search efficiency and robustness. Evaluated on a 12-dimensional six-crystal beam-splitting delay optical system, the approach consistently converges to the global optimum and substantially outperforms standard BO, TuRBO, and multi-objective BO baselines.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled parameters and highly asymmetric objective landscapes, where rewards are sparse. In such needle-in-a-haystack scenarios, even advanced methods like trust-region BO (TurBO) often lead to unsatisfactory results. We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to fundamentally simplify the search problem by transforming coordinates to decouple input features and align the active subspaces with the primary search axes. We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system, where conventional approaches, including standard BO, TuRBO and multi-objective BO, consistently led to unsatisfactory results. When combined with an reverse annealing exploration strategy, this approach reliably converges to the global optimum. The coordinate transformation itself is the key to this success, significantly accelerating the search by aligning input co-ordinate axes with the problem's active subspaces. As increasingly complex scientific instruments, from large telescopes to new spectrometers at X-ray Free Electron Lasers are deployed, the demand for robust high-dimensional optimization grows. Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning for consistent high performance while still retaining current optimization algorithms.

Problem

• Bayesian Optimization
• high-dimensional optimization
• parameter coupling
• scientific instrument alignment
• asymmetric objective landscapes

Innovation

• Domain Knowledge
• Bayesian Optimization
• Coordinate Transformation
• Active Subspace
• High-dimensional Optimization