Browsing School of Engineering and Materials Science by Author "Freitas, RSM"
Now showing items 1-5 of 5
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Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship
Freitas, RSM; Jiang, X (Elsevier BV, 2024-06)The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop ... -
An encoder-decoder deep surrogate for reverse time migration in seismic imaging under uncertainty
Freitas, RSM; Barbosa, CHS; Guerra, GM; Coutinho, ALGA; Rochinha, FA (Springer Nature, 2021-06) -
Liquid synthetic fuels design guided by chemical structure: A machine learning perspective
Freitas, RSM; Chen, C; Jiang, X (Applied Energy Innovation Institute (AEii), 2024-01-01)Physicochemical properties of synthetic fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning (ML) models are constructed to ... -
Model identification in reactor-based combustion closures using sparse symbolic regression
Freitas, RSM; Péquin, A; Galassi, RM; Attili, A; Parente, A (Elsevier, 2023-09)In Large Eddy Simulations (LES) of combustion, the accuracy of predictions might be heavily affected by deficiencies in traditional/simplified closure models, especially when employed to simulate non-conventional fuels and ... -
Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models
Freitas, RSM; Lima, APF; Chen, C; Rochinha, FA; Mira, D; Jiang, X (2022)