arXiv Machine Learning August 3, 2026 By Isabel Corona Guevara, Yeping Hu

Structured Neural Chaos: An Adaptive Surrogate Modeling Framework for Functional Uncertainty Quantification and Global Sensitivity Analysis

Read the original on arXiv Machine Learning →

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.

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

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arXiv Machine Learning

Jun 17

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

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.

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

arXiv Machine Learning

Jun 15

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

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).

By Shijie Zhong, Jiangfeng Fu, Pengfei Wei

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arXiv AI

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Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics

arXiv:2608. 16084v1 Announce Type: new Abstract: Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error growth remain poorly understood, leading to ad-hoc solutions.

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arXiv Machine Learning

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Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations

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arXiv Machine Learning

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ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces

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arXiv Machine Learning

Aug 11

The Spectral Neuron

arXiv:2608. 08003v1 Announce Type: cross Abstract: As machine learned models increase in complexity and expressive power, features of simpler models, such as interpretability and control over the shape of the modeled function are lost.

By Alex Shtoff

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

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