FORNASARI, SIMONE FRANCESCO

	Nome completo
	
			FORNASARI, SIMONE FRANCESCO	  	
			
	Afferenza
	
			Dipartimento di Matematica, Informatica e Geoscienze	  	
			
	SSD
	
			Settore GEO/10 - Geofisica della Terra Solida	  	
			
	Tipo di ruolo
	
			Assegnisti	  	
			
	Scholar ID
	
7x-PgKsAAAAJ

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UnityFVG

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Risultati 1 - 4 di 4 (tempo di esecuzione: 0.005 secondi).

A machine-learning approach for the reconstruction of ground shaking fields in real-time

2022-01-01 Fornasari, SIMONE FRANCESCO; Costa, Giovanni; Pazzi, Veronica

A Machine-Learning Approach for the Reconstruction of Ground-Shaking Fields in Real Time

2022-01-01 Fornasari, Simone Francesco; Pazzi, Veronica; Costa, Giovanni

Development of machine learning approaches for real-time ground-shaking maps reconstruction

2024-03-21 Fornasari, SIMONE FRANCESCO

Near-Real-Time Strong Motion Acquisition at National Scale and Automatic Analysis

2022-01-01 Costa, Giovanni; Brondi, Piero; Cataldi, Laura; Cirilli, Stefano; Cuius, Arianna; Ertuncay, Deniz; Falconer, Piero; Filippi, Luisa; Fornasari, Simone Francesco; Pazzi, Veronica; Turpaud, Philippe

Titolo	Data di pubblicazione	Autori
A machine-learning approach for the reconstruction of ground shaking fields in real-time	1-gen-2022	Simone Francesco FornasariGiovanni CostaVeronica Pazzi
A Machine-Learning Approach for the Reconstruction of Ground-Shaking Fields in Real Time	1-gen-2022	Fornasari, Simone FrancescoPazzi, VeronicaCosta, Giovanni
Development of machine learning approaches for real-time ground-shaking maps reconstruction	21-mar-2024	FORNASARI, SIMONE FRANCESCO
Near-Real-Time Strong Motion Acquisition at National Scale and Automatic Analysis	1-gen-2022	Costa, GiovanniBrondi, PieroCataldi, LauraCirilli, StefanoCuius, AriannaErtuncay, DenizFalconer, PieroFilippi, LuisaFornasari, Simone FrancescoPazzi, VeronicaTurpaud, Philippe + -

ArTS Archivio della ricerca di Trieste

FORNASARI, SIMONE FRANCESCO

A machine-learning approach for the reconstruction of ground shaking fields in real-time

A Machine-Learning Approach for the Reconstruction of Ground-Shaking Fields in Real Time

Development of machine learning approaches for real-time ground-shaking maps reconstruction

Near-Real-Time Strong Motion Acquisition at National Scale and Automatic Analysis