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pro vyhledávání: '"Coutinho, Alvaro L G A"'
Modern computational science and engineering applications are being improved by the advances in scientific machine learning. Data-driven methods such as Dynamic Mode Decomposition (DMD) can extract coherent structures from spatio-temporal data genera
Externí odkaz:
http://arxiv.org/abs/2208.07767
Autor:
Grave, Malú, Viguerie, Alex, Barros, Gabriel F., Reali, Alessandro, Andrade, Roberto F. S., Coutinho, Alvaro L. G. A.
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 have focused on partial differential equation (PDE) models, particula
Externí odkaz:
http://arxiv.org/abs/2205.04868
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 forward-propagated wavefield (or source wavefield) on disk. Aiming to
Externí odkaz:
http://arxiv.org/abs/2204.03380
Autor:
Viguerie, Alex, Grave, Malú, Barros, Gabriel F., Lorenzo, Guillermo, Reali, Alessandro, Coutinho, Alvaro L. G. A.
The computer simulation of organ-scale biomechanistic models of cancer personalized via routinely collected clinical and imaging data enables to obtain patient-specific predictions of tumor growth and treatment response over the anatomy of the patien
Externí odkaz:
http://arxiv.org/abs/2202.13860
Autor:
Grave, Malú, Coutinho, Alvaro L. G. A.
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 capturing methods. Two well-established interface capturing approaches ar
Externí odkaz:
http://arxiv.org/abs/2202.11476
Autor:
Viguerie, Alex, Barros, Gabriel F., Grave, Malú, Reali, Alessandro, Coutinho, Alvaro L. G. A.
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 to easily identify coherent spatio-temporal structures in data, and ef
Externí odkaz:
http://arxiv.org/abs/2110.06375
Autor:
Barros, Gabriel F., Grave, Malú, Viguerie, Alex, Reali, Alessandro, Coutinho, Alvaro L. G. A.
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 stacking collected temporal snapshots into a matrix and mapping the no
Externí odkaz:
http://arxiv.org/abs/2104.14034
Autor:
Grave, Malú, Viguerie, Alex, Barros, Gabriel F., Reali, Alessandro, Coutinho, Alvaro L. G. A.
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 which the population under study is divided into compartments based on qu
Externí odkaz:
http://arxiv.org/abs/2102.07208
Autor:
Grave, Malú, Coutinho, Alvaro L. G. A.
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, in which the population under study is divided into compartments and
Externí odkaz:
http://arxiv.org/abs/2010.11861
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 functional. The temporal adaptivity scheme is recast under the linear
Externí odkaz:
http://arxiv.org/abs/2009.14739