Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
This addresses the challenge of autonomous alignment for complex scientific instruments, such as X-ray Free Electron Lasers, by enabling robust high-dimensional optimization, though it is incremental as it builds on existing Bayesian Optimization methods.
The paper tackled the problem of optimizing high-dimensional, tightly coupled systems like scientific instruments, where standard Bayesian Optimization methods fail, by introducing a domain knowledge guided approach that transforms coordinates to decouple parameters and align with active subspaces, achieving reliable convergence to the global optimum in a 12-dimensional optical system.
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.