Deep Optimal Sensor Placement for Black Box Stochastic Simulations
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917941862203392 |
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| author | Cordero-Encinar, Paula Schröder, Tobias Yatsyshin, Peter Duncan, Andrew |
| author_facet | Cordero-Encinar, Paula Schröder, Tobias Yatsyshin, Peter Duncan, Andrew |
| contents | Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers. We propose a novel and robust approach, modelling the joint distribution over input parameters and solution with a joint energy-based model, trained on simulation data. Unlike existing simulation-based inference approaches, which must be tied to a specific set of point evaluations, we learn a functional representation of parameters and solution. This is used as a resolution-independent plug-and-play surrogate for the joint distribution, which can be conditioned over any set of points, permitting an efficient approach to sensor placement. We demonstrate the validity of our framework on a variety of stochastic problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12036 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Optimal Sensor Placement for Black Box Stochastic Simulations Cordero-Encinar, Paula Schröder, Tobias Yatsyshin, Peter Duncan, Andrew Machine Learning Applications Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers. We propose a novel and robust approach, modelling the joint distribution over input parameters and solution with a joint energy-based model, trained on simulation data. Unlike existing simulation-based inference approaches, which must be tied to a specific set of point evaluations, we learn a functional representation of parameters and solution. This is used as a resolution-independent plug-and-play surrogate for the joint distribution, which can be conditioned over any set of points, permitting an efficient approach to sensor placement. We demonstrate the validity of our framework on a variety of stochastic problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches. |
| title | Deep Optimal Sensor Placement for Black Box Stochastic Simulations |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2410.12036 |