The paper introduces a method for Bayesian optimisation in engineering design problems where both the objective and constraints depend on the state of a PDE solution. It proposes pre‑computing a set of controls whose linearised constraint response remains within tolerance, effectively pulling the state‑space constraint into design space and forming an ellipsoid from which well‑spread candidates can be drawn. The linear response map is refined online, and the approach is demonstrated on Tokamak divertor optimisation with plasma‑boundary preservation.
By Gabriel Diaz-Aylwin, Vignesh Gopakumar, Omkar Myatra, David Moulton, Lorenzo Zanisi, David S. Leslie, Henry B. Moss
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
By Raju Chowdhury, Tanmay Sen, Biswabrata Pradhan
arXiv:2607. 00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small.
By Hauke Maathuis, Roeland De Breuker, Saullo Castro, Maike Osborne
arXiv:2607. 13652v1 Announce Type: new Abstract: Many design tasks can be cast as black-box function optimization, enabling use of Bayesian optimization to find an ideal design with minimal number of trials.
By Samuli Kinnunen, Petrus Mikkola, Antti Niskanen, Arto Klami
arXiv:2606. 15512v1 Announce Type: new Abstract: Real-time reconstruction of magnetohydrodynamic equilibria is essential for plasma shaping, stability assessment and feedback control in magnetic confinement fusion.
By Jay Phil Yoo, William Howes, Yashika Ghai, Kazuma Kobayashi, Souvik Chakraborty, Syed Bahauddin Alam
arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.
By Jin Wang, Xi Lin, Handing Wang