7 citations · 18 across the 7 of their papers we have counts for
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cond-mat.mes-hall2024★ 3 cited
Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow
Daniele Lanzoni, Andrea Fantasia, Roberto Bergamaschini +2
A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard eq…
physics.comp-ph2024★ 2 cited
Quantitative analysis of the prediction performance of a Convolutional Neural Network evaluating the surface elastic energy of a strained film
Luis Martín Encinar, Daniele Lanzoni, Andrea Fantasia +3
A Deep Learning approach is devised to estimate the elastic energy density at the free surface of an undulated stressed film. About 190000 arbitrary surface profiles h(x) are r…