[2603.24602] MuViS: Multimodal Virtual Sensing Benchmark

Electrical Engineering and Systems Science > Signal Processing arXiv:2603.24602 (eess) [Submitted on 13 Mar 2026] Title:MuViS: Multimodal Virtual Sensing Benchmark Authors:Jens U. Brandt, Noah C. Puetz, Jobel Jose George, Niharika Vinay Kumar, Elena Raponi, Marc Hilbert, Thomas Bäck, Thomas Bartz-Beielstein View a PDF of the paper titled MuViS: Multimodal Virtual Sensing Benchmark, by Jens U. Brandt and 7 other authors View PDF HTML (experimental) 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. Comments: Subjects: Signal Processing (eess.SP...