A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models. - Erbet Costa Almeida; Carine de Menezes Rebello; Marcio Fontana; Leizer Schnitman; Idelfonso Bessa dos Reis Nogueira (CoRR, 2022)

A new Reinforcement Learning framework to discover natural flavor molecules. - Luana P. Queiroz; Carine M. Rebello; Erbet Almeida Costa; Vinícius V. Santana; Bruno C. L. Rodrigues; Alírio E. Rodrigues; Ana M. Ribeiro; Idelfonso B. R. Nogueira (CoRR, 2022)

A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem. - Luana P. Queiroz; Carine M. Rebello; Erbet Almeida Costa; Vinícius V. Santana; Alírio E. Rodrigues; Ana M. Ribeiro; Idelfonso B. R. Nogueira (CoRR, 2022)

Efficient hybrid modeling and sorption model discovery for non-linear advection-diffusion-sorption systems: A systematic scientific machine learning approach. - Vinícius V. Santana; Erbet Almeida Costa; Carine M. Rebello; Ana M. Ribeiro; Chris Rackauckas; Idelfonso B. R. Nogueira (CoRR, 2023)

PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction. - Vinícius Viena Santana; Carine Menezes Rebello; Luana P. Queiroz; Ana Mafalda Almeida Peixoto Ribeiro; Nadia Shardt; Idelfonso B. R. Nogueira (CoRR, 2023)

Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry. - Carine Menezes Rebello; Johannes Jäschke; Idelfonso B. R. Nogueira (CoRR, 2023)

Optimizing CO2 Capture in Pressure Swing Adsorption Units: A Deep Neural Network Approach with Optimality Evaluation and Operating Maps for Decision-Making. - Carine Menezes Rebello; Idelfonso B. R. Nogueira (CoRR, 2023)

Harnessing graph neural networks to craft fragrances based on consumer feedback. - Bruno C. L. Rodrigues; Vinícius V. Santana; Luana P. Queiroz; Carine M. Rebello; Idelfonso B. R. Nogueira (Comput. Chem. Eng., 2024)

Adaptive digital twin for pressure swing adsorption systems: Integrating a novel feedback tracking system, online learning and uncertainty assessment for enhanced performance. - Erbet Almeida Costa; Carine Menezes Rebello; Leizer Schnitman; José Miguel Loureiro; Ana M. Ribeiro; Idelfonso B. R. Nogueira (Eng. Appl. Artif. Intell., 2024)

Digital twins in chemical engineering: An integrated framework for identification, implementation, online learning, and uncertainty assessment. - Carine Menezes Rebello; Idelfonso B. R. Nogueira (Comput. Chem. Eng., 2025)