GIMC SIMAI Young 2026

Session

MS10 - Data Assimilation and Uncertainty Quantification for Complex Flows

5 Jun 2026, 14:00

Pisa

92. Scale-Adaptive Simulation with Adaptive Mesh Refinement for a digital twin framework

Pietro Tavazzi (Sant'Anna School of advanced studies)

05/06/2026, 14:00

MS10 - Data Assimilation and Uncertainty Quantification for Complex Flows

Adaptive mesh refinement (AMR) offers a practical route toward digital twins in computational fluid dynamics, automatically tailoring mesh resolution for each full-order simulation to maintain accuracy across large ensembles with varying boundary conditions and geometries. This work investigates the coupling of Scale-Adaptive Unsteady Reynolds-Averaged Navier-Stokes (SA-URANS) modelling with...

137. Coupled flow-mechanics for Uncertainty Quantification and Parameter estimation in Subsurface Resource Exploitation

Claudia Zoccarato (Universita' degli Studi di Padova)

05/06/2026, 14:15

MS10 - Data Assimilation and Uncertainty Quantification for Complex Flows

Groundwater and other subsurface resources play a fundamental role in modern water and energy systems, yet their exploitation can induce significant geomechanical responses such as land subsidence. In aquifer systems, overexploitation—defined as extraction rates exceeding natural recharge—can lead to substantial declines in piezometric levels, compaction of aquifer deposits, reduction of...

44. Data-Driven Correction of RANS Models for Urban Flow Prediction via Machine Learning and Data Assimilation

Aurora Ursetto

05/06/2026, 14:30

MS10 - Data Assimilation and Uncertainty Quantification for Complex Flows

Despite their computational efficiency, Reynolds-Averaged Navier-Stokes (RANS) models often struggle to accurately represent the complex turbulence and flow separation typical of urban environments.
These limitations highlight the need for data-driven correction strategies to improve predictive accuracy.
Enhancing RANS performance is essential for aerodynamic load estimation and for...

160. A bi-fidelity sparse-grid interpolation for Uncertainty Quantification and Sensitivity Analysis of complex flows

Filippo Fruzza (University of Pisa)

05/06/2026, 14:45

MS10 - Data Assimilation and Uncertainty Quantification for Complex Flows

High-fidelity computational models of complex flows provide accurate predictions, but their significant computational cost often makes them impractical for applications requiring repeated evaluations, such as uncertainty quantification, optimization, or the generation of databases for machine-learning training. Surrogate modeling addresses this issue by approximating the mapping between input...

54. Uncertainty Quantification of High-Order Spectral Methods for Extreme Wave Prediction

Andrea Giorgi (Università di Pisa)

05/06/2026, 15:00

MS10 - Data Assimilation and Uncertainty Quantification for Complex Flows

Rogue waves are extreme manifestations of ocean dynamics, whose prediction is strongly affected by uncertainty in the characterization of sea states. This work develops and applies advanced Uncertainty Quantification (UQ) methodologies for the probabilistic assessment of extreme wave occurrence in nonlinear irregular seas. Sea states are described through parametric spectral models, whose...