arXiv Machine Learning August 17, 2026 By Haibin Xiong, Shaoheng Dai, Peng Lan, Xuzhen He, Chenxi Tong, Sheng Zhang, Daichao Sheng

Probabilistic indirect models for undrained shear strength: addressing significant data missing and variability with advanced imputation and machine learning techniques

Read the original on arXiv Machine Learning →

arXiv:2608. 13934v1 Announce Type: new Abstract: Accurate prediction of undrained shear strength (su) is crucial for geotechnical design, but is often hampered by substantial uncertainty in traditional empirical methods.

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

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Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data

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Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning

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