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
The paper introduces BOTH, a method that differentiates topology optimization (TO) itself to compute hypergradients for tuning hyperparameters alongside the primary design optimization. By evaluating only one or two TO steps, the approach provides sufficient information and scales to thousands of hyperparameters with a cost comparable to a few standard TO runs. Experiments on stress‑constrained and compliance problems, including a neural‑parameterized density field, demonstrate the effectiveness of this joint optimization strategy.
By Suryanarayanan Manoj Sanu, Miguel Anibal Bessa, Alejandro Marcos Arag\'on
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:2609.36472v1 Announce Type: new
Abstract: Sequential Model-Based Optimization (SMBO) traditionally relies on Bayesian or ensembling surrogates for uncertainty quantification. While historically...
By Jonas Seng, Bennet Wittelsbach, Kristian Kersting
The paper introduces Decomposition-Aware Distributional Optimization (DADO), a new algorithm that exploits decomposability in property predictors to improve in‑silico design of discrete objects such as proteins, circuits, and materials. DADO uses a soft‑factorized search distribution and graph message‑passing to coordinate optimization across linked factors defined by a junction tree over design variables. The method aims to make distributional optimization over combinatorial design spaces more efficient by leveraging the structure of the predictive model.
By James C. Bowden, Sergey Levine, Jennifer Listgarten
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