ELLIS Delft Talk by Giovanni Catalani: Neural Fields for Physical Simulations. Scaling Neural Operators to aircraft physical simulations
ELLIS Delft Talk by Giovanni Catalani: Neural Fields for Physical Simulations. Scaling Neural Operators to aircraft physical simulations 26 November 2024 14:00 till 17:30 - Location: ECHO, Hall B2 - By: ELLIS Delft | Add to my calendar by Giovanni Catalani | École Nationale de l'Aéronautique et de l'Espace (ISAE-SUPAERO), Toulouse, France Abstract Recent advancements in deep learning offer powerful tools for accelerating simulations of complex systems governed by Partial Differential Equations (PDEs), particularly in computational fluid dynamics (CFD), structural mechanics, and climate modeling. Traditional numerical solvers, while highly accurate, can be computationally expensive for preliminary analysis or optimization where an extensive exploration of the design space is performed. On the other hand, Neural Fields offer a powerful framework for Operator Learning by parameterizing continuous functions over the physical space using neural networks, enabling discretization-invariant representations of arbitrary functions. In this talk, I present the foundations of Neural Operator learning for the development of scalable, data-driven approaches for large-scale simulations. Moreover, I explore the applications of these methods to aerodynamic simulations for aircraft design. Specifically, I showcase how Neural Fields can be used to construct real-time fluid dynamics simulators over aircraft geometries with shape variations across a wide range of flight conditions. This approach opens up new possibilities for efficient exploration of design spaces, allowing rapid iterations in the design and optimization process. Ultimately, these methods represent a powerful framework for the design and analysis of next-generation aircraft, significantly accelerating development cycles and improving overall efficiency. Speaker Biography Giovanni Catalani is a PhD student at Airbus and at the École Nationale de l'Aéronautique et de l'Espace (ISAE-SUPAERO) in Toulouse, France. My research focuses on the application of Deep Learning to Fluid Dynamics and physical simulations of aircraft, aiming to develop scalable, data-driven models for aerodynamic analysis and design optimization. Prior to this, I completed my Master’s degree in Aerospace Engineering at TU Delft, where my thesis at the Netherlands Aerospace Centre (NLR) focused on data-driven models for predicting unsteady aerodynamics of military aircraft.
