The goal of the workshop is to explore the intersection between (and the combination of) data and physical knowledge. The workshop aims to bring together domain experts that explore the physical understanding of the data, practitioners that develop systems and the researchers in traditional data-driven domains. The workshop welcomes papers, which focuses on addressing these issues in different applications/domains as well as algorithmic and systematic approaches to applying physical knowledge. Therefore, we further seek to develop a community that systematically analyzes the data quality regarding inference and evaluates the improvements from physical knowledge. Preliminary and on-going work is welcomed.

Topics of Interests

Topics of interests include, but are not limited to:
- Innovations in learning algorithms that combine physical knowledge or models for sensor perception and understanding
- Experiences, challenges, analysis, and comparisons of sensor data in terms of its physical properties
- Sensor data processing to improve learning accuracy
- Machine learning and deep learning with physical knowledge on sensor data
- Mobile and pervasive systems that utilize physical knowledge to enhance data acquisition
- System services such as time and location estimation enhanced by additional physical knowledge
- Heterogeneous collaborative sensing based on physical rules
- Distributed sensing for cyber-physical systems
- Advanced machine learning algorithms and solutions for efficient sensing

The application areas include but not limited to:
- Human-centric sensing applications
- Environmental and structural monitoring
- Smart cities and urban health
- Health, wellness & medical
- Smart energy systems and intelligent transportation networks
- Large Language Model and its applications in mobile system

Authors must cite and relate their submissions to relevant prior publications of their own. If applicable, ethical approval for experiments with human subjects should be demonstrated as part of the submission.