Bayesian Optimisation under State-Preservation Constraints

Abstract:In many engineering design problems, the objective and constraints depend on the state: the solution of a PDE determined by the design parameters. We consider improving a design while holding selected state observables near trusted values, which we call state preservation constraints. Constrained Bayesian optimisation handles these with a learnt feasibility model, but struggles with this problem's highly anisotropic feasible set. Our central idea is to pre-compute the set of controls whose linearised constraint response stays within tolerance, thereby pulling back the state-space constraint into design space. This linearisation defines an ellipsoid from which we can efficiently draw a large number of well-spread candidates. The underlying linear response map is refined online, and the ellipsoid is rebuilt accordingly. We demonstrate the method end-to-end on our key application - Tokamak divertor optimisation under plasma-boundary preservation.

Machine Learning (cs.LG)

arXiv:2610.12150 [cs.LG]

(or arXiv:2610.12150v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.12150 Focus to learn more arXiv-issued DOI via DataCite

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