| A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing E Salvati, A Tognan, L Laurenti, M Pelegatti, F De Bona Materials & Design 222, 111089, 2022 | 55 | 2022 |
| Evaluation and origin of residual stress in hybrid metal and extrusion bonding and comparison with friction stir welding A Tognan, L Sandnes, G Totis, M Sortino, F Berto, Ø Grong, E Salvati International Journal of Mechanical Sciences 218, 107089, 2022 | 10 | 2022 |
| Contour method with uncertainty quantification: a robust and optimised framework via gaussian process regression A Tognan, L Laurenti, E Salvati Experimental Mechanics 62 (8), 1305-1317, 2022 | 8 | 2022 |
| A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation A Tognan, A Patanè, L Laurenti, E Salvati Computer Methods in Applied Mechanics and Engineering 418, 116521, 2024 | 5 | 2024 |
| Probabilistic defect-based modelling of fatigue strength for incomplete datasets assisted by literature data A Tognan, E Salvati International Journal of Fatigue 173, 107665, 2023 | 5 | 2023 |
| Quantification of uncertainty in a defect-based physics-informed neural network for fatigue evaluation and insights on influencing factors E Avoledo, A Tognan, E Salvati Engineering Fracture Mechanics 292, 109595, 2023 | 3 | 2023 |
| On the significance of diffuse crack width self‐evolution in the phase‐field model for residually stressed brittle materials E Salvati, F Menegatti, M Kumar, M Pelegatti, A Tognan Material Design & Processing Communications 3 (5), e261, 2021 | 3 | 2021 |
| Contour Method Residual Stress Uncertainty Evaluation and Sensitivity Analysis: a Friction Stir Welded Plate Case-Study A Tognan, E Salvati | | |
