MS18 - Data-driven modelling in fluid dynamics
Chairman 1: Matteo Chiatto
Chairman 2: Antonio Colanera
Chairman 3: Luca Magri
Chairman 4: Luigi de Luca
Contatto: matteo.chiatto@unina.it
Abstract
Fluid dynamics involves structures characterized by different spatial and temporal scales. Many numerical strategies have emerged in recent years to analyze, comprehend, and foresee the behaviour of such complex systems. The symposium explores cutting-edge methods in data-driven reduced order modelling (ROM) and machine learning (ML) within fluid dynamics. The goal of the event is to unveil new horizons in the computational efficiency and accuracy of such approaches.
Chairman 2: Antonio Colanera
Chairman 3: Luca Magri
Chairman 4: Luigi de Luca
Contatto: matteo.chiatto@unina.it
Abstract
Fluid dynamics involves structures characterized by different spatial and temporal scales. Many numerical strategies have emerged in recent years to analyze, comprehend, and foresee the behaviour of such complex systems. The symposium explores cutting-edge methods in data-driven reduced order modelling (ROM) and machine learning (ML) within fluid dynamics. The goal of the event is to unveil new horizons in the computational efficiency and accuracy of such approaches.
