English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Uncertainty quantification based on stochastic spectral methods suffers fromthe curse of dimensionality. This issue was mitigated recently by low-ranktensor methods. However, there exist two fundamental challenges in low-ranktensor-based uncertainty quantification: how to automatically determine thetensor rank and how to pick the simulation samples. This paper proposes a noveltensor regression method to address these two challenges. Our method uses an$\ell_{q}/ \ell_{2}$-norm regularization to determine the tensor rank and anestimated Voronoi diagram to pick informative samples for simulation. Theproposed framework is verified by a 19-dim phonics bandpass filter and a 57-dimCMOS ring oscillator, capturing the high-dimensional uncertainty well with only90 and 290 samples respectively.

High-Dimensional Uncertainty Quantification via Active and Rank-Adaptive Tensor Regression