Enriching Machine learning-based Pipelines with the Physics-inspired Features: Case Study of Thermal Processes and Systems
#IEEEDay
#machineLearning
#ThermalProcess
#FeatureExtractions
The present seminar is focused on the role of physics-inspired feature generation in improving the performance of machine learning-based pipelines and enhancing their physical interpretability. Simulating the thermal processes taking place in indoor environments and estimating the buildings' characteristics from smart meter data are specifically presented as case studies. The impact of selecting the most promising set of features on reducing the pipelines' dimensionality and facilitating the physical interpretation of the models is also discussed.
Date and Time
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- Date: 07 Sep 2023
- Time: 02:00 PM to 03:00 PM
- All times are (UTC+02:00) Stockholm
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- Inndalsveien 28
- Bergen, Vestfold
- Norway 5063
Speakers
Behzad of : Behzad Najafi is an Assistant Professor at the Department of Energy of Politecnico di Milano and the Head of Data Science at the DataOptima Lab of the same department. His research activities are focused on machine learning-based IoT-driven predictive mo
Topic:
Enriching Machine learning-based Pipelines with the Physics-inspired Features: Case Study of Thermal Processes and Syste
Email:
Address:Milan, Italy