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arXiv · 2610.12150

Bayesian Optimisation under State-Preservation Constraints

Gabriel Diaz-Aylwin·Vignesh Gopakumar·Omkar Myatra·David Moulton·Lorenzo Zanisi·David S. Leslie·Henry B. Moss
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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.

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Gabriel Diaz-Aylwin, Vignesh Gopakumar, Omkar Myatra, David Moulton, Lorenzo Zanisi, David S. Leslie, Henry B. Moss. 2026-10-08. Bayesian Optimisation under State-Preservation Constraints. https://arxiv.org/abs/2610.12150

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