Pruning Bayesian Networks: Smarter Model Calibration for Deep-Space Digital Twins

Researchers develop new method for calibrating Bayesian networks in digital twins of spacecraft.

The approach, called 'pruning Bayesian networks', aims to improve the accuracy of digital twins by reducing the complexity of Bayesian networks.

This is particularly important for deep-space missions, where the harsh environment and limited communication channels make it challenging to calibrate models.

The new method has been tested on a digital twin of the Mars 2020 rover and shown to improve its predictive accuracy.