arXiv Machine Learning June 5, 2026 By Jihoon Kim, Heejung Youn

Field Validation of a Multi-Resolution ConvLSTM Framework for Retaining Wall Deformation Prediction

Read the original on arXiv Machine Learning →

arXiv:2606. 05556v1 Announce Type: new Abstract: This study presents a comprehensive field validation of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting retaining wall deformation during staged excavation.

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

arXiv Machine Learning

Jul 7

Exploring Convolutional Neural Processes for Weather Downscaling

arXiv:2607. 04190v1 Announce Type: new Abstract: Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularly in mountainous regions where temperature can vary by several degrees over short distances.

By Francisco Passos

benchmarks

arXiv Machine Learning

Jun 30

GPU-Accelerated Inverse Structural Anastylosis from Block Collapse Dynamics

arXiv:2606. 28394v1 Announce Type: cross Abstract: The physical anastylosis of collapsed architectural monuments -- the meticulous reassembly of fallen stone elements into their original structural configuration -- represents one of the most intellectually demanding challenges in conservation science.

By L. A. Mu\~noz

arXiv Machine Learning

Jun 25

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

arXiv:2602. 22188v2 Announce Type: replace Abstract: Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces.

By Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke, Aniket C. Joshi, Claire E. Heaney, Ahmed H. ElSheikh, Christopher C. Pain

arXiv AI

Jul 28

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers

arXiv:2607. 22804v1 Announce Type: cross Abstract: Accurate geological characterization of subsurface reservoirs from well log data is essential to support projects such as carbon capture and storage (CCS), geothermal development, and extraction of natural resources.

By Shwetha Salimath, Francesca Bugiotti, Sylvain Wlodarczyk, Sohaib Ouzineb

llms rag

arXiv AI

Jun 17

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.

By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni

safety

arXiv Machine Learning

2d ago

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

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.

By Haibin Xiong, Shaoheng Dai, Peng Lan, Xuzhen He, Chenxi Tong, Sheng Zhang, Daichao Sheng

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Exploring Convolutional Neural Processes for Weather Downscaling

GPU-Accelerated Inverse Structural Anastylosis from Block Collapse Dynamics

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

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