AI-DARWIN: A first principles-based model discovery engine using machine learning

The limitations of AI-based black-box models regarding reliability and interpretability have long been a major concern for researchers who have been arguing the case for hybrid models that integrate first-principles with machine learning techniques as a remedy. Here we propose a machine learning system that automatically discovers such mechanism-based models from data for nonlinear parametric systems. This is an extension of our previous work on linear systems using genetic feature extraction. The ap-proach works under conditions of unknown functional transformations relating the input to the output and results in functionally tractable and explainable model forms, which are mechanistically feasible. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Researchers have long been concerned about the limitations of AI-based black-box models in terms of reliability and interpretability, and have proposed hybrid models that combine first-principles with machine learning techniques as a solution. They have introduced a machine learning system that automatically discovers mechanism-based models for nonlinear parametric systems, leading to functionally traceable and explainable model forms.