| Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models RSM Freitas, ÁPF Lima, C Chen, FA Rochinha, D Mira, X Jiang Fuel 329, 125415, 2022 | 19 | 2022 |
| Parametric and model uncertainties induced by reduced order chemical mechanisms for biogas combustion RSM Freitas, FA Rochinha, D Mira, X Jiang Chemical Engineering Science 227, 115949, 2020 | 18 | 2020 |
| An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty RSM Freitas, CHS Barbosa, GM Guerra, ALGA Coutinho, FA Rochinha Computational Geosciences 25, 1229-1250, 2021 | 15 | 2021 |
| Model identification in reactor-based combustion closures using sparse symbolic regression RSM Freitas, A Péquin, RM Galassi, A Attili, A Parente Combustion and Flame 255, 112925, 2023 | 11 | 2023 |
| A predictive physics-aware hybrid reduced order model for reacting flows A Corrochano, RSM Freitas, A Parente, SL Clainche arXiv preprint arXiv:2301.09860, 2023 | 7 | 2023 |
| PF; Chen, C.; Rochinha, FA; Mira, D.; Jiang, X. Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models RSM Freitas, Á Lima Fuel 329, 125415, 2022 | 6 | 2022 |
| Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship RSM Freitas, X Jiang Energy and AI 17, 100385, 2024 | 4 | 2024 |
| Deep learning dynamical latencies for the analysis and reduction of combustion chemistry kinetics L Castellanos, R SM Freitas, A Parente, F Contino Physics of Fluids 35 (10), 2023 | 4 | 2023 |
| Constructing accurate phenomenological surrogate for fluid structure interaction models GM Guerra, R Freitas, FA Rochinha International Conference on Rotor Dynamics, 295-305, 2018 | 3 | 2018 |
| An embedded deep learning model discrepancy for computational combustion simulations RSM Freitas, FA Rochinha Journal of the Brazilian Society of Mechanical Sciences and Engineering 46 …, 2024 | 1 | 2024 |
| A time-lag autoencoder reduced-order model to predict combustion chemical kinetics L Castellanos, R Da Silva Machado De Freitas, A Parente, F Contino 45th Meeting of the Italian Section of the Combustion Institute, 2023 | 1 | 2023 |
| Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning RSM Freitas, ÁPF Lima, C Chen, FA Rochinha, D Mira, X Jiang arXiv preprint arXiv:2110.09360, 2021 | 1 | 2021 |
| Bayesian uncertainty estimation of adsorption closure models in the computational simulation of contaminant transport RSM Freitas, J Honigbaum, S Zio, GM Guerra, FA Rochinha Journal of Environmental Management 379, 124708, 2025 | | 2025 |
| A Data-Driven Approach to Refine the Partially Stirred Reactor Closure for Turbulent Premixed Flames L Piu, A Péquin, RSM Freitas, S Iavarone, H Pitsch, A Parente Flow, Turbulence and Combustion, 1-26, 2025 | | 2025 |
| Neural network potential-based molecular investigation of thermal decomposition mechanisms of ethylene and ammonia Z Xing, RSM Freitas, X Jiang Energy and AI 18, 100454, 2024 | | 2024 |
| Liquid synthetic fuels design guided by chemical structure: A machine learning perspective RSM Freitas, C Chen, X Jiang Applied Energy Innovation Institute (AEii), 2024 | | 2024 |
| A Predictive Physics-Aware Machine Learning Model for Reacting Flows Check for updates A Corrochano, RSM Freitas, A Parente, D Soledad Le Clainche New Technologies and Developments in Unmanned Systems: Proceedings of the …, 2023 | | 2023 |
| Data-driven calibration of computational combustion models employing reduced chemical Kinetics RSM Freitas Universidade Federal do Rio de Janeiro, 2020 | | 2020 |
| Constructing Accurate Phenomenological GM Guerra, R Freitas, FA Rochinha Proceedings of the 10th International Conference on Rotor Dynamics–IFToMM …, 2018 | | 2018 |
| TIME-LAG AUTO-ENCODERS FOR CHEMISTRY DIMENSIONALITY REDUCTION L Castellanos, RSM Freitas, A Parente, F Contino | | |
Fernando Alves RochinhaUniversidade Federal do Rio de JaneiroVerified email at mecanica.coppe.ufrj.br
