GIMC SIMAI Young 2026

Session

MS06.1 - Numerical Modeling for Sustainability Problems

3 Jun 2026, 16:15

Pisa

78. Discovering physical laws through Neural ODEs

Giovanni Ziarelli (MOX Laboratory, Department of Mathematics, Politecnico di Milano)

03/06/2026, 16:15

MS06 - Numerical Modeling for Sustainability Problems

We present a novel hybrid computational framework designed to reconstruct the hidden dynamics of critical parameters in complex dynamical systems [1]. In many scientific applications, the predictive accuracy of physics-based models is often limited by the inaccurate extrapolation of time-varying parameters. To address this problem, we propose an hybrid framework that integrates Neural Ordinary...

37. Stochastic First-Order Methods for Large-Scale Regularized Inverse Problems

Ilaria Trombini (University of Ferrara)

03/06/2026, 16:30

MS06 - Numerical Modeling for Sustainability Problems

A wide range of applications in imaging, data science, and machine learning can be formulated as discrete inverse problems of the form
\begin{equation}
y = Kx + \varepsilon,
\end{equation}
where $K$ is a linear operator, $x \in \mathbb{R}^d$ is the unknown variable, and $y$ represents noisy observations.
Due to ill-conditioning and the increasingly large scale of modern problems,...

66. A physics-Informed Covariance Kernel on Networks for the Uncertainty Quantification of Sea Surface Temperature

Leonardo Marchesin (MOX - Department of Mathematics, Politecnico di Milano)

03/06/2026, 16:45

MS06 - Numerical Modeling for Sustainability Problems

We present a physics-informed geostatistical framework for modeling Sea-Surface Temperature (SST) variability, bridging the gap between deterministic numerical models and stochastic uncertainty quantification. While numerical models like ERSEM provide valuable point predictions, they often lack the probabilistic framework necessary for comprehensive risk assessment of rising water...

91. Identifying Stochastic Sparse Models with SINDy algorithm

Ida Santaniello (University of Salerno)

03/06/2026, 17:00

MS06 - Numerical Modeling for Sustainability Problems

Sparse regression techniques enable the extraction of governing equations directly from measurement data, allowing efficient identification of nonlinear system dynamics with minimal complexity. This work presents algorithmic aspects of the data-driven method SINDy (Sparse Identification of Nonlinear Dynamics) [3] for the identification of the dynamics of Itô SDEs and SDDEs with a single...

164. A Conservative IMEX schema for Tumor Angiogenesis: Model, Analysis, and Efficient Simulation

Pasquale De Luca (Università di Napoli Parthenope)

03/06/2026, 17:15

MS06 - Numerical Modeling for Sustainability Problems

We present a mathematical and computational framework for the numerical simulation of tumor-induced angiogenesis, a process of central relevance in sustainable healthcare modeling and cancer treatment planning. The continuous model consists of a five-component PDE system coupling endothelial cell density $C$, protease concentration $P$, inhibitor concentration $I$, extracellular matrix density...