MuViS: Multimodal Virtual Sensing Benchmark
This addresses the problem of siloed research in virtual sensing for researchers and practitioners, offering an incremental but practical solution through a unified benchmarking platform.
The authors tackled the lack of a standardized approach in virtual sensing by introducing MuViS, a domain-agnostic benchmarking suite that consolidates diverse datasets, and found that no existing method provides a universal advantage across processes and modalities.
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.