arXiv Machine Learning June 17, 2026 By Rodrigo S. Luna, Thiago H. N. Coelho, Luiz S. L. Neto, Roberto M. Velho, Adriano M. A. Cortes, Renato N. Elias, Alexandre G. Evsukoff, Fernando A. Rochinha, Mauricio Araya-Polo, Herve Gross, Alvaro L. G. A. Coutinho

Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

Read the original on arXiv Machine Learning →

arXiv:2606. 17180v1 Announce Type: new Abstract: This chapter discusses how a data-driven machine learning approach can reproduce key aspects of the physical behavior of multiphase flows in complex geological formations.

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

Sep 21

Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...

By Carlos A. Pereira, St\'ephane Gaudreault, Valentin Dallerit, Christopher Subich, Shoyon Panday, Siqi Wei, Sasa Zhang, Siddharth Rout, Eldad Haber, Raymond J. Spiteri, David Millard

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arXiv Machine Learning

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Data-Efficient Time-Dependent PDE Surrogates: Graph Neural Simulators vs. Neural Operators

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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

arXiv Machine Learning

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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.

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arXiv AI

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OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation

arXiv:2608. 16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning.

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OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation

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Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting

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