Scientific Machine Learning method for building energy conspumtion
Parole chiave ARTIFICIAL INTELLIGENCE, ARTIFICIAL NEURAL NETWORK, ARTIFICIAL NEURAL NETWORKS, ENERGY EFFICIENCY, MACHINE LEARNING
Riferimenti LORENZO BOTTACCIOLI
Gruppi di ricerca DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, Energy Center Lab, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Descrizione 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.
Scadenza validita proposta 12/05/2023 PROPONI LA TUA CANDIDATURA