PulseAugur
EN
LIVE 20:28:14

New sampling method enhances AI surrogate model accuracy for engineering

By PulseAugur Editorial · [2 sources] ·

A new adaptive sequential sampling method has been developed for constructing polynomial chaos expansion surrogate models, specifically generalized for vector-valued quantities of interest. This method aims to improve the accuracy and stability of surrogate models by selecting new samples that balance exploration of the input space with exploitation of variance information across multiple outputs. The approach is demonstrated to be more effective than traditional Latin Hypercube Sampling for engineering problems, offering a more reliable estimation of second-order statistics. AI

IMPACT This research could lead to more efficient and accurate AI-driven simulations in engineering, reducing computational costs for complex models.

RANK_REASON The cluster contains an academic paper detailing a new method for uncertainty quantification in engineering structures using AI techniques.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New sampling method enhances AI surrogate model accuracy for engineering

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Qitian Lu, Jafar Jafari-Asl, Panagiotis Spyridis, Lukas Novak ·

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

    arXiv:2606.17233v1 Announce Type: cross Abstract: In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of complex physical systems. To alleviate the high computat…

  2. arXiv stat.ML TIER_1 English(EN) · Lukas Novak ·

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

    In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of complex physical systems. To alleviate the high computational cost of direct model evaluations, surrogate …

RELATED ENTITIES

RELATED TOPICS