ETD system

Electronic theses and dissertations repository

 

Tesi etd-09242020-172804


Thesis type
Tesi di laurea magistrale
Author
AMENDOLA, MADDALENA
URN
etd-09242020-172804
Title
Latent Assimilation: assimilating data in a latent space of a surrogate model
Struttura
INFORMATICA
Corso di studi
INFORMATICA
Supervisors
relatore Prof. Pappalardo, Luca
relatore Dott.ssa Arcucci, Rossella
Parole chiave
  • Machine Leraning
  • Data Assimilation
Data inizio appello
09/10/2020;
Consultabilità
Secretata d'ufficio
Riassunto analitico
Formulation of a new methodology that combines machine learning and data assimilation techniques. The methodology consists in using an Autoencoder to reduce the size of the input. In the latent space, a recurrent neural network (LSTM) is used as a surrogate for a dynamic system. The accuracy of the model is improved by using the Kalman Filter in the latent space which incorporates data (observation) collected by sensors, producing the updated state. The updated state is then reported in the original physical space by the decoder. The methodology was applied to a real test case.
File