| Autor: |
Giorgio Gobat, Alessia Baronchelli, Stefania Fresca, Attilio Frangi |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Actuators, Vol 12, Iss 7, p 278 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2076-0825 |
| DOI: |
10.3390/act12070278 |
| Popis: |
We propose a deep learning-based reduced order modelling approach for micro- electromechanical systems. The method allows treating parametrised, fully coupled electromechanical problems in a non-intrusive way and provides solutions across the whole device domain almost in real time, making it suitable for design optimisation and control purposes. The proposed technique specifically addresses the steady-state response, thus strongly reducing the computational burden associated with the neural network training stage and generating deep learning models with fewer parameters than similar architectures considering generic time-dependent problems. The approach is validated on a disk resonating gyroscope exhibiting auto-parametric resonance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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