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LANZONI, DANIELE

LANZONI, DANIELE  

DIPARTIMENTO DI SCIENZA DEI MATERIALI  

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Risultati 1 - 18 di 18 (tempo di esecuzione: 0.025 secondi).
Titolo Tipologia Data di pubblicazione Autori File
Accelerating Crystal Growth Simulations by Convolutional Neural Networks 02 - Intervento a convegno 2024 Lanzoni,DRovaris, FFantasia, AMontalenti, FBergamaschini, R +
Accelerating simulations of strained-film growth by deep learning: Finite element method accuracy over long time scales 01 - Articolo su rivista 2024 Lanzoni, DanieleRovaris, FabrizioFantasia, AndreaBergamaschini, RobertoMontalenti, Francesco +
Atomistic Mechanisms of dc-hd Phase Transition in Si Nanoindentation 02 - Intervento a convegno 2024 Rovaris, FLanzoni, DMarzegalli, ABarbisan, LMiglio, LScalise, EMontalenti, F +
Convolutional Recurrent Neural Networks for tackling materials dynamics at the mesoscale 02 - Intervento a convegno 2024 Lanzoni, DBergamaschini, RFantasia, AMontalenti, F
Deep Learning methods for the acceleration of growth simulations 02 - Intervento a convegno 2024 Lanzoni, D
Deep Learning methods for the investigation of temporal evolution of materials 07 - Tesi di dottorato Bicocca post 2009 2024 LANZONI, DANIELE
Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium 01 - Articolo su rivista 2024 Fantasia A.Rovaris F.Abou El Kheir O.Marzegalli A.Lanzoni D.Scalise E.Montalenti F. +
Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired neural networks for the 3D microstructure evolution of materials via Cahn–Hilliard flow 01 - Articolo su rivista 2024 Lanzoni, DanieleFantasia, AndreaBergamaschini, RobertoMontalenti, Francesco +
Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition 01 - Articolo su rivista 2024 Rovaris F.Lanzoni D.Barbisan L.Miglio L.Marzegalli A.Scalise E.Montalenti F. +
Simulating morphological evolutions by Convolutional Neural Networks 02 - Intervento a convegno 2024 Lanzoni, DRovaris, FFantasia, AMontalenti, FBergamaschini, R +
Simulations of strained films evolution: extending accessible timescales through Convolutional Neural Networks 02 - Intervento a convegno 2024 Lanzoni, DRovaris, FFantasia, ABergamaschini, RMontalenti, F +
Simulations of strained films evolution: extending accessible timescales through Convolutional Neural Networks 02 - Intervento a convegno 2024 Lanzoni, DRovaris, FBergamaschini, RFantasia, AMontalenti, F +
Unravelling Atomistic Mechanisms of Pressure-Induced Phase Transitions in Silicon Nanoindentation 02 - Intervento a convegno 2024 Fabrizio RovarisAnna marzegalliDaniele LanzoniAndrea FantasiaFrancesco MontalentiEmilio Scalise +
Accurate generation of stochastic dynamics based on multi-model generative adversarial networks 01 - Articolo su rivista 2023 Lanzoni, DMontalenti, F +
Machine learning potential for interacting dislocations in the presence of free surfaces 01 - Articolo su rivista 2022 Lanzoni D.Rovaris F.Montalenti F.
Morphological evolution via surface diffusion learned by convolutional, recurrent neural networks: Extrapolation and prediction uncertainty 01 - Articolo su rivista 2022 Daniele LanzoniMarco AlbaniRoberto BergamaschiniFrancesco Montalenti
A machine learning approach for studying low-energy dislocation distributions: methodology and applications to Ge/Si(001) films 02 - Intervento a convegno 2021 Lanzoni, DRovaris, FMontalenti, F
Computational analysis of low-energy dislocation configurations in graded layers 01 - Articolo su rivista 2020 Lanzoni D.Rovaris F.Montalenti F.