arXiv Machine Learning By Qitian Lu, Jafar Jafari-Asl, Panagiotis Spyridis, Lukas Novak

Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

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arXiv:2606. 17233v1 Announce Type: new Abstract: In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.

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arXiv Machine Learning
Aug 6

Stochastic Emulation using Generalized Stratified Sampling for Performance-Based Risk Optimization of Structures

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
arXiv Statistics ML
Sep 11

Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs

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