39 citations · 88 across the 14 of their papers we have counts for
14 papers
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…
Seismic Modeling and Migration with Random Boundaries on the NEC SX-Aurora TSUBASA
Carlos H. S. Barbosa, Alvaro L. G. A. Coutinho
Seismic imaging is a computationally demanding and data-intensive activity in the oil and gas industry. Reverse Time Migration (RTM) used in seismic applications needs to store the…
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…
Comparing the convected level-set and the Allen-Cahn phase-field methods in AMR/C simulations of two-phase flows
Malú Grave, Alvaro L. G. A. Coutinho
The modeling and simulation of two-phase flows is still an active research area, mainly when surface tension is present. One way to model the different phases is with interface cap…
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…
Adaptive Mesh Refinement and Coarsening for Diffusion-Reaction Epidemiological Models
Malú Grave, Alvaro L. G. A. Coutinho
The outbreak of COVID-19 in 2020 has led to a surge in the interest in the mathematical modeling of infectious diseases. Disease transmission may be modeled as compartmental models…
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…
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…
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…
Keeping Track of User Steering Actions in Dynamic Workflows
Renan Souza, Vítor Silva, José Camata +3
In long-lasting scientific workflow executions in HPC machines, computational scientists (the users in this work) often need to fine-tune several workflow parameters. These tunings…
Performance Evaluation of Block Diagonal Preconditioners for Divergence-Conforming B-Spline Discretization of Stokes Equations
Adriano M. A. Cortes, Alvaro L. G. A. Coutinho
The recently introduced divergence-conforming B-spline discretizations allow the construction of smooth discrete velocity-pressure pairs for viscous incompressible flows that are a…