Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910032731308032 |
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| author | Lim, Kart-Leong Dutta, Rahul Rotaru, Mihai |
| author_facet | Lim, Kart-Leong Dutta, Rahul Rotaru, Mihai |
| contents | Finite element simulations are run by package design engineers to model design structures. The process is irreversible meaning every minute structural adjustment requires a fresh input parameter run. In this paper, the problem of modeling changing (small) design structures through varying input parameters is known as inverse prediction. We demonstrate inverse prediction on the electrostatics field of an air-filled capacitor dataset where the structural change is affected by a dynamic parameter to the boundary condition. Using recent AI such as deep generative model, we outperformed best baseline on inverse prediction both visually and in terms of quantitative measure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21606 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model Lim, Kart-Leong Dutta, Rahul Rotaru, Mihai Computational Engineering, Finance, and Science Finite element simulations are run by package design engineers to model design structures. The process is irreversible meaning every minute structural adjustment requires a fresh input parameter run. In this paper, the problem of modeling changing (small) design structures through varying input parameters is known as inverse prediction. We demonstrate inverse prediction on the electrostatics field of an air-filled capacitor dataset where the structural change is affected by a dynamic parameter to the boundary condition. Using recent AI such as deep generative model, we outperformed best baseline on inverse prediction both visually and in terms of quantitative measure. |
| title | Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2602.21606 |