364 citations · 379 across the 8 of their papers we have counts for
8 papers
AGENTS4GEOS: agentic platform for open-source multi-physics simulation
Adriano M. A. Côrtes, Roberto M. Velho, Fernando A. Rochinha +3
Multi-physics simulations are essential for understanding and monitoring intricate subsurface processes such as CO2 storage. Their computational demands call for surrogate models a…
Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations
Rodrigo S. Luna, Thiago H. N. Coelho, Luiz S. L. Neto +8
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. We propo…
A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018)
Luan C. V. Silva, Lívia Sancho, Mauricio S. Silva +19
The Southwestern South Atlantic (SWSA) is a key region for climate research and renewable energy assessment, yet high-resolution meteorological data are scarce. We present a multir…
Hybrid DeepONet Surrogates for Multiphase Flow in Porous Media
Ezequiel S. Santos, Gabriel F. Barros, Amanda C. N. Oliveira +6
The solution of partial differential equations (PDEs) plays a central role in numerous applications in science and engineering, particularly those involving multiphase flow in poro…
Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning
Rodolfo S. M. Freitas, Ágatha P. F. Lima, Cheng Chen +3
Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work…
An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty
Rodolfo S. M. Freitas, Carlos H. S. Barbosa, Gabriel M. Guerra +2
Seismic imaging faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurface geophysical proper…
Digital twin, physics-based model, and machine learning applied to damage detection in structures
TG Ritto, FA Rochinha
This work is interested in digital twins, and the development of a simplified framework for them, in the context of dynamical systems. Digital twin is an ingenious concept that hel…
A workflow for seismic imaging with quantified uncertainty
Carlos H. S. Barbosa, Liliane N. O. Kunstmann, Rômulo M. Silva +6
The interpretation of seismic images faces challenges due to the presence of several uncertainty sources. Uncertainties exist in data measurements, source positioning, and subsurfa…