arXiv:2507.04553v2 Announce Type: replace-cross
Abstract: Reliability analysis traditionally relies on deterministic simulators, where identical inputs yield identical outputs. However, many real-wor...
By A. Pires, M. Moustapha, S. Marelli, B. Sudret
arXiv:2607. 28903v1 Announce Type: cross Abstract: Variance-based global sensitivity analysis (GSA) plays a key role in uncertainty quantification by identifying the contributions of uncertain inputs to the variability of the model response.
By Isabel Corona Guevara, Yeping Hu
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.
By Isabela D. Rodrigues, Seymour M. J. Spence, Henrique M. Kroetz, Andr\'e T. Beck
The paper introduces a learning-based surrogate approach for stochastic optimization problems where uncertainty depends on the decision, modeled via a nonparametric regression. It constructs a surrogate that embeds iteratively updated Jacobian estimates, using an adaptive random design that focuses sampling near the current iterate to achieve dimension‑independent convergence of the Jacobian estimates. The resulting learning‑based stochastic prox‑linear (L‑SPL) algorithm demonstrates nonasymptotic convergence rates and outperforms existing methods in sample efficiency and objective value in numerical experiments.
By Boyang Shen, Junyi Liu
arXiv:2502.00550v2 Announce Type: replace
Abstract: Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design...
By Bongseok Kim, Haoyang Zheng, Michael Penwarden, Guang Lin
arXiv:2603. 22050v2 Announce Type: replace-cross Abstract: Supervised machine learning describes the practice of fitting a parameterized model to labeled input-output data.
By Atticus Rex, Elizabeth Qian, David Peterson