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PoreML framework unifies data generation and model training for porous media flow

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PoreML, an open-source framework designed to advance machine learning applications in understanding multiphase flow within porous media. This framework integrates data generation using a GPU-native lattice Boltzmann solver with a substantial dataset of 3.3 TB, encompassing 560 simulation runs and over 158,000 time steps across various scenarios. PoreML also provides a unified learning component for evaluating model performance and physical consistency, aiming to foster community development of predictive models for critical applications like CO2 storage and fuel-cell operation. AI

IMPACT Provides a unified platform and dataset to accelerate research and development of predictive models for multiphase flow in porous media.

RANK_REASON The cluster describes a new research paper detailing a framework for machine learning in a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

PoreML framework unifies data generation and model training for porous media flow

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The cluster describes a new research paper detailing a framework for machine learning in a scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chunyang Wang, Mingrui Zhang, Yuyan Zhang, Linqi Zhu, Xin Ju, Edo Sicco Boek, Martin J. Blunt, Gege Wen ·

    PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media

    arXiv:2610.10314v1 Announce Type: new Abstract: Multiphase flow in porous microstructures is central to CO$_2$ storage, fuel-cell operation, and flip-chip packaging. Predicting these flows remains challenging because wettability and complex pore geometry govern the nonlinear evol…

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