MuViS: Multimodal Virtual Sensing Benchmark

• URL: http://arxiv.org/abs/2603.24602v1
• Date: Fri, 13 Mar 2026 14:36:43 GMT
• Title: MuViS: Multimodal Virtual Sensing Benchmark
• Abstract summary: MuViS is a domain-agnostic benchmarking suite for multimodal virtual sensing.<n>It consolidates diverse datasets into a unified interface for standardized preprocessing and evaluation.
• Score: 1.3634095123869487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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