A digital twin is a virtual representation of an object, process or system that spans its lifecycle, is updated from real-time data, and uses simulation and machine learning to help decision-making.
Our parsimonious neural network technology is an asset for digital twin applications:
- Accuracy (which is a direct consequence of parsimony) allows the early detection of the smallest drift between the model and the real system
- Early model update, on the basis of a short sequence of data, maintains the virtuous cycle of accuracy
- Early detection of attacks and anomalies
- Evaluation and update of the model can be embedded on small chips
- Long term dynamic prediction