Scientific Machine Learning and Applications

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• Advancing Graph Neural Networks for CO2 Plume Migration in Complex Geological Formations

  Adriano Cortes, Roberto M. Velho, Rodrigo de S. Luna, Thiago H. N. Coelho, Renato N. Elias, Alexandre Evsukoff, Herve Gross, Mauricio Araya, Fernando A. Rochinha, Alvaro L. G. A. Coutinho

• U-net-based Segmentation Of Sensor Data For Detecting Important Events In Oil Wells

  Lucas Gouveia Omena Lopes, Thales Miranda de Almeida Vieira, Andressa Celestino Araújo Da Silva, Igor de Melo Nery, Davi Leão Ramos, Eduardo Toledo de Lima Junior, William Wagner Matos Lira, Pedro Esteves Aranha

• A machine learning-based method for analyzing the axial load capacity of rectangular reinforced concrete columns.

  juliana souza , Emerson Felipe Felix , Leandro Lopes da Silva

• A Proposal for Quality Classification of Cocoa Beans Using Convolutional Neural Networks

  BRENDA BAYERL PINHEIRO, Luiz Alberto Pinto

• Analysis of Steel Structural Profiles under Flexural-Compression stress using Machine Learning Algorithms

  David Coimbra, André Sarkis Müller

• Automatic Detection of Cardiac Pathologies from ECG Signals Using Variational Autoencoders (VAE)

  Elis De Mori Ravagnani, Daniel Cruz Cavalieri

• Enhanced EEG Classification of Foot Motor Imagery Using Unscented Kalman Filtering for Neurorehabilitation

  Rodrigo Oliveira Altoé, Gabriel da Silva Braz, Cristian Felipe Blanco‑Diaz, Cristian David Guerrero‑Mendez, Rafhael Milanezi de Andrade, Guilherme Rangel Furtado

• Ensemble methodology incorporating uncertainty quantification applied to modeling COVID-19 outbreaks

  Douglas de Albuquerque, Renato Simões Silva, Gilson Antonio Giraldi, Regina Célia Cerqueira de Almeida

• Estimation of concrete compressive strength using machine learning

  Dimas Ribeiro, Pedro Cesar Rodrigues Alves Abrão, Carlos Jose Massucato, Vanderley Moacyr John, André Carlos Ponce de Leon Ferreira de Carvalho

• Forecasting Wind and Solar Energy Generation Using Recurrent Neural Networks: A Comparison Between LSTM and GRU

  Fábio Pinto Monte , Daniel Barreto Merlo, Cassius Zanetti Resende, Fidelis Zanetti de Castro

• Hybrid DeepONet Architectures for Porous Media Flow Simulation

  EZEQUIEL SOUZA DOS SANTOS, Alvaro L.G.A. Coutinho, Gabriel F. Barros, Romulo M. Silva, Amanda C.N. Oliveira, Rodolfo S.M. Freitas, Fernando A. Rochinha

• Machine Learning to Assist Structural Engineers: A Bibliometric Analysis 2020-2025

  Jesús Daniel Villalba Morales, Ana Sofía González Merizalde, Jesús Antonio García Sánchez

• Neural Network Application for Aerodynamic Force Estimation in Heavy Artillery Projectile Trajectories

  Lucas gomes do amaral, Daniel Henrique Braz de Sousa, André Rezende

• Non-Intrusive Data-driven Surrogate Models for Predicting Turbidity Currents Deposition from 3D Simulations

  Roberto Machado Velho, Gabriel M. Guerra, José Camata, Renato N. Elias, Fernando A. Rochinha, Adriano M. Cortes, Alvaro L. G. A. Coutinho, Tiago H. F. Jesus, Thais C. A. Empinotti, Paulo L. B. Paraizo

• Numerical Simulation Using Nonlinear Finite Elements and Machine Learning for the Evaluation of Failure Probability of Reinforced Concrete Beams

  Oswald Casaverde Lopez, Leandro Lopes da Silva

• Online Model Order Reduction for Flow Simulations problems using Dynamic Mode Decomposition

  Felipe Toledo, Alvaro Coutinho, Gabriel F. Barros, Renato Nascimento Elias

• Performance Assessment on Neural Operator Surrogate Models For Porous Media Flows

  GABRIEL F. BARROS, Rômulo M. Silva, Amanda C. N. Oliveira, Ezequiel S. Santos, Rodolfo S. M. Freitas, Fernando A. Rochinha, Alvaro L. G. A. Coutinho

• Use of Physics-Informed Neural Networks to compute velocity and pressure fields around an airfoil in transonic flow

  Vinícius Passeri Moraes de Souza, Thiago da Silva Batista, Vinícius de Carvalho Rispoli