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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.24602 |
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| _version_ | 1866914556345843712 |
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| author | Brandt, Jens U. Puetz, Noah C. George, Jobel Jose Kumar, Niharika Vinay Raponi, Elena Hilbert, Marc Bäck, Thomas Bartz-Beielstein, Thomas |
| author_facet | Brandt, Jens U. Puetz, Noah C. George, Jobel Jose Kumar, Niharika Vinay Raponi, Elena Hilbert, Marc Bäck, Thomas Bartz-Beielstein, Thomas |
| contents | Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from first-principle and hybrid models to modern data-driven methods research remains siloed, leaving no established default approach that transfers across processes, modalities, and sensing configurations. We introduce MuViS, a domain-agnostic benchmarking suite for multimodal virtual sensing that consolidates diverse datasets into a unified interface for standardized preprocessing and evaluation. Using this framework, we benchmark established approaches spanning gradient-boosted decision trees and deep neural network (NN) architectures, and show that none of these provides a universal advantage, underscoring the need for generalizable virtual sensing architectures. MuViS is released as an open-source, extensible platform for reproducible comparison and future integration of new datasets and model classes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24602 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MuViS: Multimodal Virtual Sensing Benchmark Brandt, Jens U. Puetz, Noah C. George, Jobel Jose Kumar, Niharika Vinay Raponi, Elena Hilbert, Marc Bäck, Thomas Bartz-Beielstein, Thomas Signal Processing Artificial Intelligence Virtual sensing aims to infer hard-to-measure quantities from accessible measurements and is central to perception and control in physical systems. Despite rapid progress from first-principle and hybrid models to modern data-driven methods research remains siloed, leaving no established default approach that transfers across processes, modalities, and sensing configurations. We introduce MuViS, a domain-agnostic benchmarking suite for multimodal virtual sensing that consolidates diverse datasets into a unified interface for standardized preprocessing and evaluation. Using this framework, we benchmark established approaches spanning gradient-boosted decision trees and deep neural network (NN) architectures, and show that none of these provides a universal advantage, underscoring the need for generalizable virtual sensing architectures. MuViS is released as an open-source, extensible platform for reproducible comparison and future integration of new datasets and model classes. |
| title | MuViS: Multimodal Virtual Sensing Benchmark |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2603.24602 |