Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs

• URL: http://arxiv.org/abs/2511.22567v1
• Date: Thu, 27 Nov 2025 16:00:45 GMT
• Title: Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs
• Authors: Feyza Eksen, Stefan Oehmcke, Stefan Lüdtke,
• Abstract summary: We propose expected reduction in epistemic uncertainty as a new acquisition function for sensor placement.<n>We extend Conditionalal Neural Processes (ConvCNPs) with a Mixture Density Networks (MDNs) output head for uncertainty estimation.<n>Preliminary results suggest that epistemic uncertainty driven sensor placement more effectively reduces model error than approaches based on overall uncertainty.
• Score: 5.6674894905527795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate sensor placement is critical for modeling spatio-temporal systems such as environmental and climate processes. Neural Processes (NPs), particularly Convolutional Conditional Neural Processes (ConvCNPs), provide scalable probabilistic models with uncertainty estimates, making them well-suited for data-driven sensor placement. However, existing approaches rely on total predictive uncertainty, which conflates epistemic and aleatoric components, that may lead to suboptimal sensor selection in ambiguous regions. To address this, we propose expected reduction in epistemic uncertainty as a new acquisition function for sensor placement. To enable this, we extend ConvCNPs with a Mixture Density Networks (MDNs) output head for epistemic uncertainty estimation. Preliminary results suggest that epistemic uncertainty driven sensor placement more effectively reduces model error than approaches based on overall uncertainty.

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