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A machine learning approach to evaluate the influence of higher-order generalized variables on shell free vibrations
2024 Petrolo, M.; Iannotti, P.; Trombini, M.; Pagani, A.; Carrera, E.
Selection of beam, plate, and shell theories using an axiomatic/asymptotic method and neural networks
2024 Petrolo, M.; Iannotti, P.; Pagani, A.; Carrera, E.
Selection of best beam theories based on natural frequencies and dynamic response obtained through mode superposition method
2024 Iannotti, P.
Synergistic Use of CUF and Machine Learning for Structural Mechanics Problems
2024 Petrolo, M.; Pagani, A.; Carrera, E.; Iannotti, P.; Candita, G.
Best kinematics for shell finite elements using convolutional neural networks
2023 Petrolo, M.; Iannotti, P.
Best Theory Diagrams for Laminated Composite Shells Based on Failure Indexes
2023 Petrolo, M.; Iannotti, P.
Derivation of Best Theory Diagrams through the use of Failure Indexes
2023 Petrolo, M.; Iannotti, P.; Pagani, A.; Carrera, E.
Global-local modeling of composite structures through node-dependent kinematics and convolutional neural networks
2023 Petrolo, M.; Iannotti, P.; Trombini, M.; Melis, M.
Refinement of Structural Theories for Composite Shells through Convolutional Neural Networks
2023 Petrolo, M.; Iannotti, P.; Trombini, M.; Melis, M.
Advanced finite elements and neural networks for scaled models
2022 Carrera, E.; Iannotti, P.; Pagani, A.; Petrolo, M.
Local Refinement of Structural Kinematics for Failure Onset Analysis via Neural Networks
2022 Petrolo, M.; Pagani, A.; Iannotti, P.; Carrera, E.
On the accuracy and efficiency of convolutional neural networks for element-wise refinement of FEM models
2022 Petrolo, M.; Iannotti, P.; Pagani, A.; Carrera, E.
| Citazione | Data di pubblicazione | Autori | File |
|---|---|---|---|
| A machine learning approach to evaluate the influence of higher-order generalized variables on shell free vibrations / Petrolo, M.; Iannotti, P.; Trombini, M.; Pagani, A.; Carrera, E.. - In: JOURNAL OF SOUND AND VIBRATION. - ISSN 0022-460X. - ELETTRONICO. - 575:(2024). [10.1016/j.jsv.2024.118255] | 1-gen-2024 | M. PetroloP. IannottiM. TrombiniA. PaganiE. Carrera | PITPC_JSV_2024.pdf |
| Selection of beam, plate, and shell theories using an axiomatic/asymptotic method and neural networks / Petrolo, M.; Iannotti, P.; Pagani, A.; Carrera, E.. - (2024). (Intervento presentato al convegno ASME 2024 Aerospace Structures, Structural Dynamics, and Materials Conference SSDM2024 April 29 - May 1, 2024, Renton, Washington tenutosi a Renton, WA, USA nel 29 April - 1 May 2024). | 1-gen-2024 | M. PetroloP. IannottiA. PaganiE. Carrera | SSDM2024-121308.pdf |
| Selection of best beam theories based on natural frequencies and dynamic response obtained through mode superposition method / Iannotti, P.. - ELETTRONICO. - 42:(2024), pp. 5-9. (Intervento presentato al convegno IV Aerospace PhD-Days tenutosi a Scopello, Italy nel 6-9th of May 2024) [10.21741/9781644903193-2]. | 1-gen-2024 | Iannotti P. | - |
| Synergistic Use of CUF and Machine Learning for Structural Mechanics Problems / Petrolo, M.; Pagani, A.; Carrera, E.; Iannotti, P.; Candita, G.. - (2024). (Intervento presentato al convegno Fourth International Congress on Mechanics of Advanced Materials and Structures - ICMAMS tenutosi a Bengaluru, India nel 11-13 December 2024). | 1-gen-2024 | M. PetroloA. PaganiE. CarreraP. IannottiG. Candita | - |
| Best kinematics for shell finite elements using convolutional neural networks / Petrolo, M.; Iannotti, P.. - In: MECHANICS OF ADVANCED MATERIALS AND STRUCTURES. - ISSN 1537-6532. - ELETTRONICO. - 30:5(2023), pp. 1106-1116. [10.1080/15376494.2022.2111009] | 1-gen-2023 | M. PetroloP. Iannotti | PI_MAMS_2022_revised.pdf; PI_MAMS_2023.pdf |
| Best Theory Diagrams for Laminated Composite Shells Based on Failure Indexes / Petrolo, M.; Iannotti, P.. - In: AEROTECNICA MISSILI & SPAZIO. - ISSN 2524-6968. - ELETTRONICO. - 102:(2023), pp. 199-218. [10.1007/s42496-023-00158-5] | 1-gen-2023 | M. PetroloP. Iannotti | PI_ATMS_2023.pdf |
| Derivation of Best Theory Diagrams through the use of Failure Indexes / Petrolo, M.; Iannotti, P.; Pagani, A.; Carrera, E.. - ELETTRONICO. - (2023). (Intervento presentato al convegno AIAA SCITECH 2023 Forum tenutosi a National Harbor nel 23-27 January 2023) [10.2514/6.2023-0592]. | 1-gen-2023 | M. PetroloP. IannottiA. PaganiE. Carrera | 6.2023-0592.pdf |
| Global-local modeling of composite structures through node-dependent kinematics and convolutional neural networks / Petrolo, M.; Iannotti, P.; Trombini, M.; Melis, M.. - (2023). (Intervento presentato al convegno ICCS26 - 26th International Conference on Composite Structures & MECHCOMP8 - 8th International Conference on Mechanics of Composites tenutosi a Porto nel 27-30 June 2023). | 1-gen-2023 | M. PetroloP. IannottiM. Trombini + | ICCS26_proceedings_020723.pdf |
| Refinement of Structural Theories for Composite Shells through Convolutional Neural Networks / Petrolo, M.; Iannotti, P.; Trombini, M.; Melis, M.. - (2023). (Intervento presentato al convegno 27th Congress of the Italian Association of Aeronautics and Astronautics, AIDAA 2023 tenutosi a Padova nel 4-7 September 2023) [10.21741/9781644902813-31]. | 1-gen-2023 | M. PetroloP. IannottiM. Trombini + | PITM_AIDAA2023_Paper.pdf |
| Advanced finite elements and neural networks for scaled models / Carrera, E.; Iannotti, P.; Pagani, A.; Petrolo, M.. - ELETTRONICO. - (2022). (Intervento presentato al convegno 9th International Symposium on Scale Modeling tenutosi a Napoli nel 2-4 March 2022). | 1-gen-2022 | E. CarreraP. IannottiA. PaganiM. Petrolo | CARRER_abs.pdf |
| Local Refinement of Structural Kinematics for Failure Onset Analysis via Neural Networks / Petrolo, M.; Pagani, A.; Iannotti, P.; Carrera, E.. - ELETTRONICO. - (2022). (Intervento presentato al convegno 15th World Congress on Computational Mechanics (WCCM-XV) and 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII) tenutosi a Yokohama nel 31/07/2022 - 05/08/2022). | 1-gen-2022 | M. PetroloA. PaganiP. IannottiE. Carrera | PPIC_WCCM2022.pdf |
| On the accuracy and efficiency of convolutional neural networks for element-wise refinement of FEM models / Petrolo, M.; Iannotti, P.; Pagani, A.; Carrera, E.. - ELETTRONICO. - (2022). (Intervento presentato al convegno ASME 2022 International Mechanical Engineering Congress and Exposition IMECE2022 tenutosi a Columbus, Ohio nel 30 October 2022 - 3 November 2022). | 1-gen-2022 | M. PetroloP. IannottiA. PaganiE. Carrera | - |