arXiv:2607. 12241v1 Announce Type: cross Abstract: Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology.
By Ayushi Jolotia, Parikshit Pareek
arXiv:2605. 07565v2 Announce Type: replace-cross Abstract: We study Bayesian Optimisation (BO) in settings where the objective function is influenced by uncontrollable environmental contexts governed by an unknown probability distribution.
By Tigran Ramazyan, Denis Derkach
arXiv:2607. 14652v1 Announce Type: new Abstract: Topology optimisation (TO) often requires repeated finite element analysis and sensitivity-based material updates, which can be costly when multiple candidate designs are needed under varying physical and design conditions.
By Shusheng Xiao, Jinshuai Bai, Hyogu Jeong, Yunfei Xi, Yilin Gui, YuanTong Gu
arXiv:2510. 22048v4 Announce Type: replace Abstract: Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces).
By Ana K. Rivera, Anvita Bhagavathula, Alvaro Carbonero, Priya Donti
arXiv:2607. 22788v1 Announce Type: cross Abstract: AC optimal power flow determines the minimum-cost generation dispatch under nonlinear power balance constraints and is solved thousands of times daily in electricity market operations.
By Zhilin Huang
arXiv:2607. 24777v1 Announce Type: new Abstract: Architected metamaterials derive their functions from structure, creating vast opportunities to program physical responses through topology design.
By Haolin Li, Yuyang Miao, Menglei Li, Jinshuai Bai, Liyuan Wang, Xin Liu, Bo Gao, Jiantao Liu, Danilo Mandic, Zahra Sharif Khodaei, M. H. Aliabadi, Weiqiu Chen