Contact Stephan Wernli
Background
Stephan completed his BSc in engineering and management, with a major in supply chain management, at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW). Following this, he obtained an MSc in Applied Information and Data Science at the Lucerne University of Applied Sciences and Arts (FHZ) with a focus on time series analytics, agent-based simulations, and deep learning.
Stephan currently works as a data scientist and data engineer in the field of industrial engineering at Endress+Hauser Flow.
Current activities
Stephan's research focuses on determining the remaining useful lifetime (RLU) under data scarcity for flow measurement sensors. He tries to combine several deep-learning approaches, focussing on domain adaptation and data augmentation methodologies. The proposed data augmentation method is based on transferring learned knowledge from synthetically generated data to real operational systems
Clients
Endress+Hauser Flow AG
Publications
Articles In Journals
- Wernli S, Hollmach M, Franzmann C, Kessler D, Fernandes H, .... (2026). Enhancing vortex-flow-meter precision using physics-informed contrastive learning. Flow Measurement and Instrumentation, 109
- Wernli S, Huber LG, Avdelidis NP & Rieder A. (2025). Coriolis massflow measurement errors due to inhomogeneous entrained particles: an analytical model. Flow Measurement and Instrumentation, 103