A Robust Learning Methodology for Uncertainty-aware Scientific Machine Learning models
This work addresses the need for uncertainty-aware models in Scientific Machine Learning, particularly for applications like soft sensors, but it appears incremental as it builds on existing literature by integrating multiple uncertainty components.
The authors tackled the problem of robust learning in Scientific Machine Learning by proposing a comprehensive methodology for uncertainty evaluation that considers multiple uncertainty sources, validated through a case study on a polymerization reactor soft sensor, which demonstrated robustness to uncertainties.
Robust learning is an important issue in Scientific Machine Learning (SciML). There are several works in the literature addressing this topic. However, there is an increasing demand for methods that can simultaneously consider all the different uncertainty components involved in SciML model identification. Hence, this work proposes a comprehensive methodology for uncertainty evaluation of the SciML that also considers several possible sources of uncertainties involved in the identification process. The uncertainties considered in the proposed method are the absence of theory and causal models, the sensitiveness to data corruption or imperfection, and the computational effort. Therefore, it was possible to provide an overall strategy for the uncertainty-aware models in the SciML field. The methodology is validated through a case study, developing a Soft Sensor for a polymerization reactor. The results demonstrated that the identified Soft Sensor are robust for uncertainties, corroborating with the consistency of the proposed approach.