collaborators

51 papers

math.NA2026

A Dynamical Approximation Scheme on the Stiefel manifold for Wasserstein Gradient Flows

Isabella Carla Gonnella, Olga Mula, Federico Pichi +1

We propose a meshless Lagrangian dynamical method for approximating Wasserstein gradient flows (WGFs). The evolving measure is represented as the pushforward of the initial measure…

cs.LG2026

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…

math.NA2026

A Generative Model-Free Form Deformation Approach for the Generation of Mesh Motions with Applications to PDE

Gugliemo Padula, Artem Sinitsa, Gianluigi Rozza

The paper presents a topology‑agnostic framework that models 3D shape deformations as an ODE flow parameterized by a time‑dependent free‑form deformation lattice, and couples it wi…

math.NA2026

Bifurcation curve detection with deflation for multiparametric PDEs

Nitin Kumar, Federico Pichi, Gianluigi Rozza

This work presents a comprehensive framework for capturing bifurcating phenomena and detecting bifurcation curves in nonlinear multiparametric partial differential equations, where…

math.NA2026

Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry

G. Li Causi, N. Tonicello, L. Magri +1

Autoencoders (AEs) have emerged as powerful tools for non-linear dimensionality reduction, often surpassing traditional linear methods such as Proper Orthogonal Decomposition (POD)…

math.NA2026

An isotropic recovery-based error estimator algorithm for mesh adaptation in a finite volume environment with application to atmospheric flows

Lander Besabe, Michele Girfoglio, Simona Perotto +2

We develop an Isotropic Recovery-based Error Estimator (IREE) to drive mesh adaptation within a finite volume framework. Recovery-based error estimators are widely used in practice…