arXiv Machine Learning June 30, 2026 By Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke, Julien Maes, Sebastian Geiger, Muhammad Z. Kashim, Zainol A. A. Bakar, Kamaljit Singh

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

Read the original on arXiv Machine Learning →

arXiv:2602. 06989v2 Announce Type: replace-cross Abstract: Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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 Machine Learning

Sep 24

An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media

The paper introduces a physics-informed neural network (PINN) framework for modeling fluid flow in dual‑network porous media, specifically addressing double porosity/permeability (DPP) systems. The framework embeds governing equations and boundary conditions into the loss function with adaptive weighting, employs dynamic collocation point selection, and uses shared trunk architectures to efficiently capture coupled pore‑network behavior. It is mesh‑free, accurately handles discontinuities across layered domains, and supports robust inverse analysis for parameter identification, with a systematic convergence study validating its stability and accuracy.

By V. S. Maduri, K. B. Nakshatrala

arXiv Machine Learning

Jun 30

PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks

arXiv:2510. 19465v2 Announce Type: replace-cross Abstract: Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization, as natural spatial heterogeneity causes extracted sub-images to deviate significantly from core-measured values.

By Ali Sadeghkhani, Brandon Bennett, Masoud Babaei, Arash Rabbani

diffusion safety

arXiv AI

Jul 15

From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data

arXiv:2607. 12175v1 Announce Type: cross Abstract: X-ray tomography enables nondestructive characterization of material microstructures, while advances in micro-CT imaging have accelerated volumetric data acquisition and reconstruction.

By Pradyumna Elavarthi, Arun J. Bhattacharjee, Harrison Lisabeth, Anca Ralescu, Petrus H. Zwart, Dilworth Parkinson, Elizabeth G. Clark

llms computer-vision fine-tuning

arXiv AI

Sep 10

Data-driven rational function neural networks: a new method for generating analytical models of rock physics

arXiv:2109.08813v1 Announce Type: cross Abstract: Seismic wave velocity of underground rock plays important role in detecting internal structure of the Earth. Rock physics models have long been the f...

By Weitao Sun

arXiv Machine Learning

Jun 30

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications

arXiv:2606. 28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensionality, memory constraints, and limited training data.

By Christian Munoz, Alexandre Tartakovsky

One email a morning, machine-written

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

An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media

PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks

From Reconstruction to Interpretation: Zero-Setup Multi-Phase Segmentation of X-ray Tomography Data

Data-driven rational function neural networks: a new method for generating analytical models of rock physics

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications