arXiv Machine Learning By Shijie Zhong, Jiangfeng Fu, Pengfei Wei

Hybrid Uncertainty Sensitivity Analysis Based on the HSIC for High-Dimensional Responses with Aleatory--Epistemic Separation

Read the original on arXiv Machine Learning →

arXiv:2606. 14053v1 Announce Type: cross Abstract: Quantifying the influence of hybrid aleatory and epistemic uncertainties on high-dimensional system responses remains a major challenge in global sensitivity analysis (GSA).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 10

Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows

arXiv:2606. 09857v1 Announce Type: new Abstract: Reduced-order models (ROMs) provide an efficient surrogate for complex multiscale systems, but their predictive accuracy is often compromised by truncation errors and the inadequate representation of interactions between resolved and unresolved scales.

By Jice Zeng, Shady E. Ahmed, David Barajas-Solano, Panos Stinis