| Physics-informed neural networks approach for 1D and 2D Gray-Scott systems F Giampaolo, M De Rosa, P Qi, S Izzo, S Cuomo Advanced Modeling and Simulation in Engineering Sciences 9 (1), 5, 2022 | 27 | 2022 |
| Solving groundwater flow equation using physics-informed neural networks S Cuomo, M De Rosa, F Giampaolo, S Izzo, VS Di Cola Computers & Mathematics with Applications 145, 106-123, 2023 | 15 | 2023 |
| A local boundedness result for a class of obstacle problems with non-standard growth conditions M De Rosa, AG Grimaldi Journal of Optimization Theory and Applications 195 (1), 282-296, 2022 | 9 | 2022 |
| Speckle noise removal via learned variational models S Cuomo, M De Rosa, S Izzo, F Piccialli, M Pragliola Applied Numerical Mathematics 200, 162-178, 2024 | 5 | 2024 |
| A physics-informed deep learning approach for solving strongly degenerate parabolic problems P Ambrosio, S Cuomo, M De Rosa Engineering with Computers, 1-17, 2024 | 5 | 2024 |
| A numerical approach for soil microbiota growth prediction through physics-informed neural network S Cuomo, M De Rosa, F Piccialli, L Pompameo, V Vocca Applied Numerical Mathematics 207, 97-110, 2025 | 2 | 2025 |
| Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy S Cuomo, M De Rosa, F Piccialli, L Pompameo Mathematics and Computers in Simulation 223, 368-379, 2024 | 2 | 2024 |
| Modelling the COVID-19 infection rate through a Physics-Informed learning approach M De Rosa, F Giampaolo, F Piccialli, S Cuomo 2023 31st Euromicro International Conference on Parallel, Distributed and …, 2023 | 2 | 2023 |
| Numerical Model for Data Railway Fusion: diagnostic applications S Cuomoa, M De Rosaa, A Mannarab, G Mastelloneb, F Picciallia PROCEEDINGS OF SIMAI 2023, 314, 0 | | |
