33 citations · 77 across the 12 of their papers we have counts for
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cond-mat.stat-mech2025
Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
Daniele Lanzoni, Olivier Pierre-Louis, Roberto Bergamaschini +1
We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. S…
cond-mat.mtrl-sci2025
Unified machine-learning framework for property prediction and time-evolution simulation of strained alloy microstructure
Andrea Fantasia, Daniele Lanzoni, Niccolò Di Eugenio +3
We introduce a unified machine-learning framework designed to conveniently tackle the temporal evolution of alloy microstructures under the influence of an elastic field. This appr…