Author

S. Pagani

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#machine learning Preprint Oct 2026

Learning PDE solution operators with variable initial conditions via Latent Dynamics Networks

In many-query scenarios, data-driven surrogate models provide an efficient alternative to high-fidelity solvers for simulating physical systems governed by Partial Differential Equations (PDEs). In this context, the Latent Dynamics Network (LDNet) has recently demonstrated remarkable performance in predicting the respo...

Stefano Maria Pizzamiglio, S. Pagani, Francesco Regazzoni · 0 citations

Jul 2026

Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics

A physics-informed neural network framework that integrates the incompressible Navier-Stokes equations with velocity measurements coming from experimental flow field data is proposed that enhances the reconstruction of velocity fields, enables the estimation of unmeasured quantities such as pressure and wall shear stre...

Irena Radisic, Raffaele Tirotta, Alberto Zingaro et al. · arXiv.org · 0 citations