Introduction
This school aims to introduce hybrid numerical modeling methodologies that combine partial differential equation models with high-fidelity data. These methodologies focus on achieving explainability, accuracy, and robustness while maintaining speed and efficiency for performance prediction, parametric optimization, control, and data assimilation in complex industrial systems.
The preferred methodology in this school is Reduced Order Models (ROM). These parametric mathematical models are derived from partial differential equations by utilizing offline-calculated and stored solutions.
In certain applications, the solution space has low dimensionality, allowing for minimal degradation of accuracy against computational speed and model scalability. Thus, ROMs can address the computational complexity associated with the curse of dimensionality by significantly reducing it.
While ROMs have matured to a certain extent in the past decade, challenges persist. Parametric problems governed by advection fields or solutions with substantially compact support features, such as shockwaves, face difficulties in limited dimensional reduction and insufficient model generalization for out-of-sample solutions. These issues often result from a linear or affine approximation of the solution space, and the school will focus on specific techniques to address them.
The school is co-organized between ARIA project, EDF and CEA.
Date
01 juillet – 05 juillet 2024
Place
EDF Lab
Paris-Saclay
Boulevard Gaspard Monge
91120 Palaiseau
Registration
If you wish to participate, please fill the registration form and send it before 12 mai 2024 at Régis Vizet.
More information
More information is available here