DE ROSA, MARIAPIA
DE ROSA, MARIAPIA
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Physics-informed neural networks approach for 1D and 2D Gray-Scott systems
2022 Giampaolo, F.; De Rosa, M.; Qi, P.; Izzo, S.; Cuomo, S.
Modelling the COVID-19 infection rate through a Physics-Informed learning approach
2023 De Rosa, M.; Giampaolo, F.; Piccialli, F.; Cuomo, S.
Solving groundwater flow equation using physics-informed neural networks
2023 Cuomo, S.; De Rosa, M.; Giampaolo, F.; Izzo, S.; Schiano Di Cola, V.
Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy
2024 Cuomo, S.; De Rosa, M.; Piccialli, F.; Pompameo, L.
Speckle noise removal via learned variational models
2024 Cuomo, S.; De Rosa, M.; Izzo, S.; Piccialli, F.; Pragliola, M.
Unveiling the Potential of Machine Learning in Activity Recognition for Industry 4.0
2024 Chiaro, D.; Pian, Qi; DE ROSA, Mariapia; Cuomo, S.; Piccialli, F.
A physics-informed deep learning approach for solving strongly degenerate parabolic problems
2024 Ambrosio, Pasquale; Cuomo, Salvatore; De Rosa, Mariapia
A Local Boundedness Result for a Class of Obstacle Problems with Non-Standard Growth Conditions
2022 De Rosa, Mariapia; Grimaldi, Antonio Giuseppe
A numerical approach for soil microbiota growth prediction through physics-informed neural network
2025 Cuomo, S.; De Rosa, M.; Piccialli, F.; Pompameo, L.; Vocca, V.
| Titolo | Tipologia | Data di pubblicazione | Autore(i) | File |
|---|---|---|---|---|
| Physics-informed neural networks approach for 1D and 2D Gray-Scott systems | 1.1 Articolo in rivista | 2022 | Giampaolo, F.; De Rosa, M.; Qi, P.; Izzo, S.; Cuomo, S. | |
| Modelling the COVID-19 infection rate through a Physics-Informed learning approach | 4.1 Articoli in Atti di convegno | 2023 | De Rosa, M.; Giampaolo, F.; Piccialli, F.; Cuomo, S. | |
| Solving groundwater flow equation using physics-informed neural networks | 1.1 Articolo in rivista | 2023 | Cuomo, S.; De Rosa, M.; Giampaolo, F.; Izzo, S.; Schiano Di Cola, V. | |
| Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy | 1.1 Articolo in rivista | 2024 | Cuomo, S.; De Rosa, M.; Piccialli, F.; Pompameo, L. | |
| Speckle noise removal via learned variational models | 1.1 Articolo in rivista | 2024 | Cuomo, S.; De Rosa, M.; Izzo, S.; Piccialli, F.; Pragliola, M. | |
| Unveiling the Potential of Machine Learning in Activity Recognition for Industry 4.0 | 2.1 Contributo in volume (Capitolo o Saggio) | 2024 | Chiaro, D.; Pian, Qi; DE ROSA, Mariapia; Cuomo, S.; Piccialli, F. | |
| A physics-informed deep learning approach for solving strongly degenerate parabolic problems | 1.1 Articolo in rivista | 2024 | Ambrosio, Pasquale; Cuomo, Salvatore; De Rosa, Mariapia | |
| A Local Boundedness Result for a Class of Obstacle Problems with Non-Standard Growth Conditions | 1.1 Articolo in rivista | 2022 | De Rosa, Mariapia; Grimaldi, Antonio Giuseppe | |
| A numerical approach for soil microbiota growth prediction through physics-informed neural network | 1.1 Articolo in rivista | 2025 | Cuomo, S.; De Rosa, M.; Piccialli, F.; Pompameo, L.; Vocca, V. |