Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning

Understand

Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge.

• This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems.
• Moreover, these sensors can be in motion and can become online or offline over time.
• The key leverage in addressing this scientific issue is the wealth of data accumulated from the sensors.