arXiv:2604. 25241v2 Announce Type: replace Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of admissible instances, while each candidate design may require expensive stochastic finite-element evaluations.
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.
arXiv:2607. 13688v1 Announce Type: new Abstract: Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior.
The paper presents a surrogate‑assisted optimization framework for designing a seven‑parameter current‑excited electromagnetic coil, coupling a 2‑D axisymmetric FEM model with a Matern 5/2 Gaussian‑process surrogate. Sequential Bayesian optimization using expected improvement (EI) is compared with COBYLA and BOBYQA, showing that the ranking of methods depends on the FEM evaluation budget and that different methods excel at early progress, terminal response, or computational cost. A retrospective study indicates no clear advantage of EI over posterior‑mean ranking on this smooth response surface, and the results are specific to the axisymmetric benchmark used.
arXiv:2607. 23480v1 Announce Type: new Abstract: Variational autoencoders (VAEs) transform high-dimensional, often noisy data into a compact latent representation, making downstream optimization more tractable.
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.
arXiv:2608.21976v1 Announce Type: new Abstract: Agentic AI has automated parts of scientific discovery, including paper generation, expert-level coding, therapeutic proposal, and autonomous experimen...
arXiv:2606. 09037v1 Announce Type: new Abstract: Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions.
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.
arXiv:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.
arXiv:2607. 10896v1 Announce Type: new Abstract: Small-data inverse design is challenging in engineering informatics when observations are heterogeneous, mixed-type, and constrained by physical relations among design variables.
arXiv:2608. 05006v1 Announce Type: new Abstract: Metamodels are instrumental in reducing the computational burden associated with nested reliability analyses and optimization loops in Performance-Based Risk Optimization (PBRO) of structures under stochastic loads.
The paper investigates nonlinear dimensionality reduction for Bayesian optimisation (BO) by transforming high‑dimensional black‑box optimisation problems into a sequence of low‑dimensional latent‑space BO (LSBO) tasks. It extends earlier linear embedding approaches by using variational autoencoders (VAEs), deep metric loss, and adaptive retraining to better capture nonlinear structure, and couples LSBO with sequential domain reduction (SDR‑LSBO) to progressively narrow search domains. Experiments on GPU‑accelerated BoTorch with Matérn‑5/2 Gaussian‑process surrogates show that VAE‑based LSBO outperforms adaptive linear embeddings, and the authors provide a theoretical analysis of latent‑space error versus representation gap under PAC‑Bayes conditions.