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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.17758 |
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| _version_ | 1866916411787444224 |
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| author | Thel, Simon Greve, Lars Karl, Maximilian van der Smagt, Patrick |
| author_facet | Thel, Simon Greve, Lars Karl, Maximilian van der Smagt, Patrick |
| contents | The Finite Element Method (FEM) is a widely used technique for simulating crash scenarios with high accuracy and reliability. To reduce the significant computational costs associated with FEM, the Finite Element Method Integrated Networks (FEMIN) framework integrates neural networks (NNs) with FEM solvers. However, this integration can introduce errors and deviations from full-FEM simulations, highlighting the need for an additional metric to assess prediction confidence, especially when no ground truth data is available. In this study, we adapt the Deep Variational Bayes Filter (DVBF) to the FEMIN framework, incorporating a probabilistic approach to provide qualitative insights into prediction confidence during FEMIN simulations. The adaptation involves using the learned transition model for a predictive decoding step, generating a preliminary force prediction. This predictive force is used alongside the displacement and the velocity data from the FEM solver as input for the encoder model. The decoder reconstructs the likelihood distribution based on the posterior. The mean force of this distribution is applied to the FEM solver, while the predicted standard deviation can be used for uncertainty estimation. Our findings demonstrate that the DVBF outperforms deterministic NN architectures in terms of accuracy. Furthermore, the standard deviation derived from the decoder serves as a valuable qualitative metric for assessing the confidence in FEMIN simulations. This approach enhances the robustness of FEMIN by providing a measure of reliability alongside the simulation results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17758 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adapting Deep Variational Bayes Filter for Enhanced Confidence Estimation in Finite Element Method Integrated Networks (FEMIN) Thel, Simon Greve, Lars Karl, Maximilian van der Smagt, Patrick Computational Engineering, Finance, and Science The Finite Element Method (FEM) is a widely used technique for simulating crash scenarios with high accuracy and reliability. To reduce the significant computational costs associated with FEM, the Finite Element Method Integrated Networks (FEMIN) framework integrates neural networks (NNs) with FEM solvers. However, this integration can introduce errors and deviations from full-FEM simulations, highlighting the need for an additional metric to assess prediction confidence, especially when no ground truth data is available. In this study, we adapt the Deep Variational Bayes Filter (DVBF) to the FEMIN framework, incorporating a probabilistic approach to provide qualitative insights into prediction confidence during FEMIN simulations. The adaptation involves using the learned transition model for a predictive decoding step, generating a preliminary force prediction. This predictive force is used alongside the displacement and the velocity data from the FEM solver as input for the encoder model. The decoder reconstructs the likelihood distribution based on the posterior. The mean force of this distribution is applied to the FEM solver, while the predicted standard deviation can be used for uncertainty estimation. Our findings demonstrate that the DVBF outperforms deterministic NN architectures in terms of accuracy. Furthermore, the standard deviation derived from the decoder serves as a valuable qualitative metric for assessing the confidence in FEMIN simulations. This approach enhances the robustness of FEMIN by providing a measure of reliability alongside the simulation results. |
| title | Adapting Deep Variational Bayes Filter for Enhanced Confidence Estimation in Finite Element Method Integrated Networks (FEMIN) |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2409.17758 |