39 citations · 61 across the 9 of their papers we have counts for
9 papers
Advances in Scientific Machine Learning for Coupled Fluid Flow and Transport
Gabriel F. Barros, Rômulo M. Silva, Alvaro L. G. A. Coutinho
This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and…
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…
Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression
Gabriel F. Barros, Malú Grave, José J. Camata +1
Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition…
Modeling nonlocal behavior in epidemics via a reaction-diffusion system incorporating population movement along a network
Malú Grave, Alex Viguerie, Gabriel F. Barros +3
The outbreak of COVID-19, beginning in 2019 and continuing through the time of writing, has led to renewed interest in the mathematical modeling of infectious disease. Recent works…
Data-driven simulation of Fisher-Kolmogorov tumor growth models using Dynamic Mode Decomposition
Alex Viguerie, Malú Grave, Gabriel F. Barros +3
The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions…
Coupled and Uncoupled Dynamic Mode Decomposition in Multi-Compartmental Systems with Applications to Epidemiological and Additive Manufacturing Problems
Alex Viguerie, Gabriel F. Barros, Malú Grave +2
Dynamic Mode Decomposition (DMD) is an unsupervised machine learning method that has attracted considerable attention in recent years owing to its equation-free structure, ability…
Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations
Gabriel F. Barros, Malú Grave, Alex Viguerie +2
Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract spatio-temporal coherent structures that dictate a given dynamical system. The method consists of…
Assessing the spatio-temporal spread of COVID-19 via compartmental models with diffusion in Italy, USA, and Brazil
Malú Grave, Alex Viguerie, Gabriel F. Barros +2
The outbreak of COVID-19 in 2020 has led to a surge in interest in the mathematical modeling of infectious diseases. Such models are usually defined as compartmental models, in whi…
Finite element solution of nonlocal Cahn-Hilliard equations with feedback control time step size adaptivity
Gabriel F. Barros, Adriano M. A. Côrtes, Alvaro L. G. A. Coutinho
In this study, we evaluate the performance of feedback control-based time step adaptivity schemes for the nonlocal Cahn-Hilliard equation derived from the Ohta-Kawasaki free energy…