Tu-SML-07 - Empowering Self-Powered Sensors via Agentic RAG: A Domain-Specific LLM Framework for Building Physics Retrieval

Event

EUROSENSORS 2026
2026-09-06 - 2026-09-09
Zurich

Band

Poster

Chapter

Signal Analysis, Machine Learning, Physics-Informed Machine Learning And Artificial Intelligence For Sensing

Author(s)

S. Saadatnasab, M. Li, Z. Wang - Hong Kong University of Science and Technology,Hong Kong

Pages

708 - 708

DOI

10.5162/eurosensors2026/Tu-SML-07

ISBN

978-3-910600-12-6

Price

free

Abstract

Wireless sensor networks deployed in built environments face a critical bottleneck: reliable in-situ powering. Energy harvesting, whether from thermal gradients, ambient airflow, or mechanical vibrations—demands precise, context-aware calculations of building physics parameters such as heat flux, air velocity, temperature differentials, and fluid properties. However, general-purpose Large Language Models lack the domain-specific reasoning required to retrieve and compute these engineering metrics from technical literature, codes, and equipment schedules, as they consistently fail to accurately interpret structured tables, complex mathematical formulas, and chart-based empirical data. To bridge this gap, we present a domain-specific LLM framework powered by an advanced multimodal Agentic Retrieval-Augmented Generation (RAG) pipeline...