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.
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:2603. 07965v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality.
By Jing Jingzhe, Fan Zheyi, Szu Hui Ng, Qingpei Hu
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:2605. 05857v2 Announce Type: replace Abstract: Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved.
By Rohit Sonker, Hiro Josep Farre Kaga, Jiayu Chen, Andrew Rothstein, Ian Char, Ricardo Shousha, Egemen Kolemen, Jeff Schneider
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
arXiv:2608. 01344v2 Announce Type: replace Abstract: Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configurations that jointly meet requirements on confinement, field-line topology, force balance, stability proxies, and geometry.
By Tingjia Zhang, Zhuoran Meng, Runlai Xu
arXiv:2604. 17838v2 Announce Type: replace Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.
By Kijung Jeon, Michael Muehlebach, Molei Tao
arXiv:2502. 18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation.
By Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Al\'an Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
The paper introduces ORACLE, a method for constrained learning that evaluates constraint compatibility after the optimizer has taken a step, rather than before. ORACLE performs a joint endpoint linearization of diverse constraint families, constructs an alignment in the optimizer’s geometry, bounds its authority, and commits the adjustment only after validation. Experiments on eight PDE benchmarks with four different optimizers show that ORACLE matches or improves performance in 94% of configurations, outperforming other constraint-handling approaches.
By Utkarsh Grover, Wyatt Mackey, Kaixun Hua, J. Morris Chang, Xiaomin Lin