6 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…
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…
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…