48 citations · 95 across the 82 of their papers we have counts for
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Trainable Spline Representations for Physics-Informed Learning
Giovanni Canali, Nicola Demo, Gianluigi Rozza
This work introduces Physics-Informed Splines (PI-Splines), a structured spline-based architecture for physics-informed learning. Instead of representing the solution of a differen…
Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
Lorenzo Tomada, Federico Pichi, Gianluigi Rozza
Graph Neural Networks (GNNs) are emerging as powerful tools for nonlinear Model Order Reduction (MOR) of time-dependent parameterized Partial Differential Equations (PDEs). However…
Revisiting Deep Information Propagation: Fractal Frontier and Finite-size Effects
Giuseppe Alessio D'Inverno, Zhiyuan Hu, Leo Davy +3
Information propagation characterizes how input correlations evolve across layers in deep neural networks. This framework has been well studied using mean-field theory, which assum…
BARNN: A Bayesian Autoregressive and Recurrent Neural Network
Dario Coscia, Max Welling, Nicola Demo +1
Autoregressive and recurrent networks have achieved remarkable progress across various fields, from weather forecasting to molecular generation and Large Language Models. Despite t…