Margarita Chasapi (Aachen University)<\/strong> : TBA<\/p>\n\n\n\n Emmanuel Franck (Inria Center at Universit\u00e9 de Lorraine)<\/strong>: “From SciML approaches to hybrid methods”<\/em><\/p>\n\n\n\n Physics-informed Neural Networks (PINNs), Neural Operators (NOs) and Reduced Order Models (ROMs) are among the dominant approaches in learning for PDEs (SciML). This talk begins with classical numerical methods to show how these approaches can be naturally linked to them, as generalisations or reformulations. We will discuss their strengths in terms of expressivity and flexibility, as well as their limitations, which are often linked to a lack of theoretical guarantees and numerical control. In the second part, we will introduce hybrid methods, which fit within this SciML framework whilst seeking a compromise between expressivity and guarantees, by combining numerical structure and learning.<\/p>\n\n\n\n Lorenzo Sala (INRAE, Universit\u00e9 Paris – Saclay)<\/strong>: TBA<\/p>\n","protected":false},"excerpt":{"rendered":" Invited speakers Margarita Chasapi (Aachen University) : TBA Emmanuel Franck (Inria Center at Universit\u00e9 de Lorraine): “From SciML approaches to hybrid methods” Physics-informed Neural Networks (PINNs), Neural Operators (NOs) and Reduced Order Models (ROMs) are among the dominant approaches in learning for PDEs (SciML). This talk begins with classical numerical methods\u2026<\/p>\n
\n\n\n\n
\n\n\n\n