Scientific Machine Learning method for building energy conspumtion
keywords ARTIFICIAL INTELLIGENCE, ARTIFICIAL NEURAL NETWORK, ARTIFICIAL NEURAL NETWORKS, ARTIFICIAL NEURAL NETWORKS, CONTROL PROCESS, ENERGY, ENERGY EFFICIENCY, ENERGY MODELING, MACHINE LEARNING, NEURAL NETWORKS
Reference persons LORENZO BOTTACCIOLI
Description Nowadays, Scientific Machine Learning (SciML) is revolutionizing the academic and industrial world like a storm. It combines traditional scientific mechanistic modelling (differential equations) with the machine and deep learning methodologies. As it is well known, traditional Deep Learning suffers some issues like interpretability and enforcing physical constraints; combining such methodologies with numerical analysis and differential equations can bring to a new field of research through new methods, architectures and algorithms. SciML techniques aim to overcome the classical barriers of the data-driven approaches like (i) the significant amount of data required from data-driven models to identify and interpret events/signals, (ii) the generation and collection of data often not fitting the purpose. The thesis will investigate the application of such methods in the building energy consumption field.
Deadline 12/05/2023 PROPONI LA TUA CANDIDATURA