Mechanistic AI

Mechanistic AI is physics driven and is able to accurately estimate and predict many common phenomena in process industry such as heat transfer surface fouling, valve stiction and reaction rates.

The method is both data efficient and resilient against problems in data. It also offers an explicit way of incorporating domain knowledge into the model.

Compared to deep-learning methods, the development of Mechanist AI models is significantly more time efficient as it does not require experimenting with various architectures and hyperparameter tuning to make most of the data.


Mechanistic AI estimates and predicts unknown phenomena using overall understanding of physics and domain knowledge. 

Mechanistic AI predictions are significantly more accurate compared to any other method (e.g., seq2seq-LSTM) which typically require 50X more data to even come close to MAI accuracy.

MAI enables efficient control of processes with lacking or malfunctioning instrumentation as well as serves as an engine for predictive maintenance.